best experimental research

211+ Best Experimental Research Topics for Students [2024]

Experimental research serves as a cornerstone in scientific inquiry, allowing researchers to test hypotheses through controlled experiments. 

For students, engaging in experimental research not only fosters a deeper understanding of theoretical concepts but also cultivates critical thinking and problem-solving skills essential for academic success. 

By exploring experimental research topics, students gain hands-on experience, honing their analytical abilities while gaining practical insights into their chosen fields of study. 

In this blog, we will delve into a myriad of experimental research topics for students across various disciplines, providing inspiration and guidance for conducting meaningful experiments and advancing academic endeavors.

What is Experimental Research?

Table of Contents

Experimental research is a systematic approach to scientific inquiry where researchers manipulate one or more variables to observe the effect on another variable, known as the dependent variable, while controlling other factors. 

This method aims to establish cause-and-effect relationships between variables, providing empirical evidence to support or refute hypotheses. Through controlled experiments conducted in laboratory or field settings, researchers can investigate phenomena, test theories, and draw conclusions about the underlying mechanisms governing natural phenomena. 

Experimental research plays a crucial role in advancing knowledge across various disciplines, from psychology and medicine to physics and engineering, by providing empirical evidence to support theoretical claims.

Importance of Experimental Research Topics for Students

Experimental research topics for students are crucial for several reasons:

Hands-on Learning

Experimental research topics offer students practical experience in applying theoretical knowledge to real-world scenarios, enhancing their understanding of complex concepts.

Critical Thinking Skills

Engaging in experimental research cultivates critical thinking skills as students design experiments, analyze data, and draw conclusions, fostering a deeper understanding of scientific methodologies.

Problem-Solving Abilities

By tackling experimental challenges, students develop problem-solving abilities essential for navigating academic and professional environments.

Personalized Learning

Students can explore topics aligned with their interests and passions, fostering a sense of ownership and motivation in their academic pursuits.

Preparation for Future Endeavors

Experimental research equips students with essential skills and experiences valuable for future academic pursuits, research endeavors, and professional careers.

List of Experimental Research Topics for Students

Here’s a list of experimental research topics for students across various fields can explore:

• The effects of mindfulness meditation on stress reduction.
• Investigating the impact of social media usage on self-esteem.
• Examining the relationship between sleep quality and academic performance.
• The influence of music on cognitive function and memory.
• Exploring the bystander effect in emergency situations.
• Investigating the effects of color on mood and productivity.
• The relationship between exercise and mental health outcomes.
• Examining the efficacy of cognitive-behavioral therapy in anxiety management.
• Investigating the effects of peer pressure on decision-making.
• The impact of parental involvement on children’s academic achievement.
• Exploring the psychology of addiction and its treatment.
• Investigating the role of genetics in personality traits.
• Examining the effects of early childhood trauma on adult mental health.
• The influence of cultural factors on perception and behavior.
• Investigating the placebo effect and its implications for medical treatment.
• Investigating the effects of different diets on gut microbiota composition.
• Examining the impact of environmental pollutants on amphibian populations.
• Investigating the efficacy of natural remedies in treating common ailments.
• Exploring the genetics of aging and longevity.
• The effects of climate change on plant phenology and growth patterns.
• Investigating the role of gut-brain axis in mental health disorders.
• Examining the effects of exercise on cardiovascular health.
• Exploring the mechanisms of antibiotic resistance in bacteria.
• Investigating the ecological impacts of invasive species.
• Examining the effects of light pollution on nocturnal animals.
• Exploring the genetics of rare diseases and potential treatments.
• Investigating the biodiversity of coral reef ecosystems.
• Examining the effects of different pollutants on aquatic organisms.
• Exploring the role of epigenetics in gene expression.
• Investigating the evolutionary origins of human behavior.
• Investigating the properties of superconductors at different temperatures.
• Exploring the behavior of quantum particles in entangled states.
• Investigating the relationship between temperature and electrical conductivity in metals.
• Examining the principles of wave-particle duality in quantum mechanics.
• Exploring the physics of renewable energy sources such as solar and wind power.
• Investigating the properties of materials under extreme pressure conditions.
• Examining the behavior of fluids in microgravity environments.
• Exploring the principles of chaos theory and deterministic systems.
• Investigating the physics of sound and its applications in acoustics.
• Examining the behavior of particles in accelerators and colliders.
• Exploring the properties of electromagnetic waves and their applications.
• Investigating the phenomenon of gravitational waves and their detection.
• Examining the principles of thermodynamics and heat transfer.
• Exploring the physics of nanomaterials and their applications.
• Investigating the principles of quantum computing and its potential applications.
• Investigating the properties of different catalysts in chemical reactions.
• Exploring the principles of green chemistry and sustainable synthesis methods.
• Investigating the kinetics of enzyme-catalyzed reactions.
• Examining the behavior of nanoparticles in solution.
• Exploring the chemistry of medicinal plants and natural remedies.
• Investigating the effects of pH on chemical reactions.
• Examining the properties of polymers and their applications.
• Exploring the chemistry of atmospheric pollutants and their effects on the environment.
• Investigating the principles of electrochemistry and battery technology.
• Examining the synthesis and properties of novel materials for electronic devices.
• Exploring the chemistry of food additives and preservatives.
• Investigating the mechanisms of drug metabolism in the human body.
• Examining the properties of supercritical fluids and their applications.
• Exploring the chemistry of fermentation and its industrial applications.
• Investigating the synthesis and properties of nanomaterials for biomedical applications.

Computer Science

• Investigating the effectiveness of machine learning algorithms in predicting stock prices.
• Exploring the security vulnerabilities of blockchain technology.
• Investigating the impact of virtual reality on learning outcomes.
• Examining the effectiveness of different programming languages in software development.
• Exploring the potential of quantum computing in solving complex problems.
• Investigating the impact of social media algorithms on user behavior.
• Examining the privacy implications of data mining techniques.
• Exploring the principles of artificial intelligence and its ethical considerations.
• Investigating the effectiveness of cybersecurity measures in protecting against cyber threats.
• Examining the potential of augmented reality in enhancing user experiences.
• Exploring the applications of natural language processing in text analysis.
• Investigating the impact of mobile technology on daily life.
• Examining the effectiveness of different encryption techniques in securing data.
• Exploring the principles of distributed computing and its applications.
• Investigating the potential of autonomous vehicles in improving transportation systems.

Environmental Science

• Investigating the impact of deforestation on biodiversity loss.
• Exploring the effects of climate change on ocean acidification.
• Investigating the efficacy of renewable energy technologies in reducing greenhouse gas emissions.
• Examining the effects of pollution on air quality and public health.
• Exploring the restoration of degraded ecosystems and their ecological benefits.
• Investigating the relationship between urbanization and heat island effects.
• Examining the effects of plastic pollution on marine ecosystems.
• Exploring the principles of sustainable agriculture and food production.
• Investigating the impacts of invasive species on native biodiversity.
• Examining the effectiveness of conservation strategies in protecting endangered species.
• Exploring the effects of water pollution on aquatic ecosystems and human health.
• Investigating the potential of carbon sequestration techniques in mitigating climate change.
• Examining the impacts of land use changes on ecosystem services.
• Exploring the principles of ecological modeling and their applications in conservation.
• Investigating the effects of habitat fragmentation on wildlife populations.
• Investigating the effects of social media on interpersonal relationships.
• Exploring the impact of income inequality on social mobility.
• Investigating the factors influencing voting behavior in democratic societies.
• Examining the effects of globalization on cultural diversity.
• Exploring the dynamics of family structures and their impact on child development.
• Investigating the correlation between socioeconomic status and access to education.
• Examining the effects of mass media on shaping public opinion.
• Exploring the relationship between gender equality and economic development.
• Investigating the impact of immigration on social cohesion.
• Examining the role of religion in shaping societal norms and values.
• Exploring the dynamics of social movements and their impact on policy change.
• Investigating the effects of racial discrimination on mental health outcomes.
• Examining the relationship between crime rates and socioeconomic factors.
• Exploring the influence of cultural norms on gender roles and identity.
• Investigating the impact of technology on social interactions and community cohesion.
• Investigating the effectiveness of flipped classrooms in improving student learning outcomes.
• Exploring the impact of inclusive education on students with disabilities.
• Investigating the effects of parental involvement on student achievement.
• Examining the role of teacher-student relationships in academic success.
• Exploring the efficacy of project-based learning in fostering critical thinking skills.
• Investigating the impact of standardized testing on student stress levels.
• Examining the effectiveness of online learning platforms in distance education.
• Exploring the benefits of early childhood education on long-term academic success.
• Investigating the effects of classroom environment on student motivation.
• Examining the impact of socioeconomic factors on educational attainment.
• Exploring the role of technology in personalized learning and adaptive instruction.
• Investigating the effectiveness of bilingual education programs in language acquisition.
• Examining the impact of school nutrition programs on student health and academic performance.
• Exploring the benefits of arts education on cognitive development and creativity.
• Investigating the relationship between school climate and student behavior.
• Investigating the impact of minimum wage laws on employment levels.
• Exploring the effects of globalization on income inequality.
• Investigating the relationship between economic growth and environmental sustainability.
• Examining the effects of government subsidies on agricultural markets.
• Exploring the impact of foreign direct investment on economic development.
• Investigating the effects of trade tariffs on international trade flows.
• Examining the relationship between inflation and interest rates.
• Exploring the impact of unemployment on mental health and well-being.
• Investigating the effectiveness of fiscal policy in mitigating economic recessions.
• Examining the role of entrepreneurship in economic growth and innovation.
• Exploring the effects of income taxation on labor supply and consumer behavior.
• Investigating the relationship between education levels and earning potential.
• Examining the impacts of economic sanctions on target countries.
• Exploring the principles of behavioral economics and decision-making.
• Investigating the role of central banks in monetary policy and economic stability.

Political Science

• Investigating the factors influencing voter turnout in elections.
• Exploring the effects of political polarization on democratic institutions.
• Investigating the impact of media framing on public opinion.
• Examining the role of political parties in shaping policy agendas.
• Exploring the dynamics of international diplomacy and conflict resolution.
• Investigating the effects of electoral systems on political representation.
• Examining the relationship between political ideology and policy preferences.
• Exploring the impact of campaign finance regulations on electoral outcomes.
• Investigating the effects of gerrymandering on political representation.
• Examining the role of interest groups in the policy-making process.
• Exploring the impact of political propaganda on public perceptions.
• Investigating the effects of term limits on political accountability.
• Examining the role of social movements in driving political change.
• Exploring the dynamics of political leadership and decision-making.
• Investigating the impact of globalization on national sovereignty.

Health Sciences

• Investigating the effects of lifestyle factors on cardiovascular health.
• Exploring the efficacy of alternative medicine approaches in pain management.
• Investigating the relationship between diet and mental health outcomes.
• Examining the effects of stress on immune system function.
• Exploring the efficacy of vaccination programs in preventing infectious diseases.
• Investigating the impact of healthcare disparities on health outcomes.
• Examining the effects of air pollution on respiratory health.
• Exploring the relationship between sleep quality and cognitive function.
• Investigating the efficacy of telemedicine in delivering healthcare services.
• Examining the effects of aging on musculoskeletal health.
• Exploring the relationship between gut microbiota and metabolic disorders.
• Investigating the impact of exercise on mental health and well-being.
• Examining the effects of environmental toxins on reproductive health.
• Exploring the efficacy of mindfulness-based interventions in stress management.
• Investigating the relationship between social support and health outcomes.

Engineering

• Investigating the efficiency of renewable energy technologies in power generation.
• Exploring the potential of 3D printing in manufacturing and prototyping.
• Investigating the effects of material properties on structural integrity in engineering design.
• Examining the efficiency of water treatment technologies in wastewater management.
• Exploring the potential of nanotechnology in drug delivery systems.
• Investigating the impact of transportation infrastructure on urban development.
• Examining the effects of seismic retrofitting on building resilience in earthquake-prone areas.
• Exploring the principles of artificial intelligence in autonomous vehicle navigation.
• Investigating the efficacy of biodegradable materials in sustainable packaging.
• Examining the potential of robotics in healthcare applications.
• Exploring the effects of climate change on civil engineering infrastructure.
• Investigating the efficiency of smart grid technologies in electricity distribution.
• Examining the impact of renewable energy integration on power grid stability.
• Exploring the potential of biomimicry in engineering design.
• Investigating the principles of quantum computing in information technology.
• Investigating the effects of corporate social responsibility initiatives on brand reputation.
• Exploring the impact of organizational culture on employee satisfaction and productivity.
• Investigating the relationship between customer satisfaction and loyalty in service industries.
• Examining the effects of e-commerce on traditional retail markets.
• Exploring the impact of supply chain disruptions on business resilience.
• Investigating the effectiveness of marketing strategies in influencing consumer behavior.
• Examining the relationship between leadership styles and organizational performance.
• Exploring the effects of globalization on multinational corporations.
• Investigating the impact of technology adoption on business innovation.
• Examining the effects of workplace diversity on team performance and creativity.
• Exploring the relationship between financial incentives and employee motivation.
• Investigating the effects of mergers and acquisitions on corporate profitability.
• Examining the impact of digital transformation on business operations.
• Exploring the principles of risk management and its applications in business decision-making.
• Investigating the relationship between organizational structure and agility in fast-paced markets.

Literature and Language Studies

• Investigating the impact of translation on the reception of literary works in different cultures.
• Exploring the evolution of language through historical literature analysis .
• Investigating the portrayal of gender roles in contemporary literature.
• Examining the influence of literary movements on societal attitudes and values.
• Exploring the use of symbolism in literary works and its interpretation.
• Investigating the effects of bilingualism on cognitive development and language proficiency.
• Examining the relationship between language and identity in immigrant communities.
• Exploring the depiction of mental illness in literature and its impact on stigma.
• Investigating the role of literature in fostering empathy and understanding.
• Examining the influence of political ideology on literary censorship.
• Exploring the use of narrative techniques in autobiographical literature.
• Investigating the portrayal of cultural diversity in contemporary literature.
• Examining the relationship between language and power in political discourse.
• Exploring the representation of marginalized voices in literature.
• Investigating the effects of translation strategies on the fidelity of literary texts.
• Investigating the influence of digital media on storytelling techniques in contemporary literature.
• Exploring the portrayal of environmental themes and sustainability in literature across different cultural contexts.

These experimental research topics for students span various disciplines, offering students a wide range of avenues for exploration and inquiry in their academic pursuits.

Tips for Conducting Experimental Research Topics

Conducting experimental research can be a challenging but rewarding endeavor. Here are some tips to help students effectively plan and carry out their experiments:

• Clearly define your research question and objectives to guide your experimental design.
• Develop a detailed experimental protocol outlining procedures, variables, and controls.
• Ensure proper randomization and blinding techniques to minimize bias and ensure validity.
• Collect data meticulously, recording observations accurately and consistently.
• Analyze data rigorously using appropriate statistical methods to draw meaningful conclusions.
• Consider ethical considerations throughout the research process, obtaining necessary approvals and consent.
• Communicate findings effectively through clear and concise reporting in academic formats.
• Iterate and refine your experimental approach based on feedback and further analysis for continuous improvement.

Wrapping Up

Exploring experimental research topics for students is a valuable opportunity for intellectual growth and academic development. 

Through hands-on inquiry and investigation, students can deepen their understanding of theoretical concepts, hone critical thinking skills, and cultivate a passion for scientific exploration. 

Engaging in experimental research fosters creativity, resilience, and problem-solving abilities essential for success in both academic and professional realms. Moreover, the interdisciplinary nature of experimental research encourages students to bridge gaps between various fields, fostering a holistic approach to knowledge acquisition. 

By embracing experimentation, students not only contribute to the advancement of scientific knowledge but also empower themselves to become lifelong learners and innovative thinkers prepared to tackle the challenges of the future.

1. How do I narrow down my topic?

Start by brainstorming broad areas of interest and gradually narrow down your focus based on feasibility, resources, and academic relevance.

2. Can I change my topic midway through the research?

While it’s best to stick with your chosen topic, sometimes unforeseen circumstances may require adjustments. Consult with your supervisor or mentor before making any significant changes.

3. How long does it take to conduct experimental research?

The duration of experimental research varies depending on the complexity of the topic, availability of resources, and experimental design. It could range from a few weeks to several months or even years.

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200+ Experimental Quantitative Research Topics For STEM Students In 2023

STEM stands for Science, Technology, Engineering, and Math, but these are not the only subjects we learn in school. STEM is like a treasure chest of skills that help students become great problem solvers, ready to tackle the real world’s challenges.

In this blog, we are exploring the world of Research Topics for STEM Students. We will explain what STEM really means and why it is so important for students. We will also give you the lowdown on how to pick a fascinating research topic. We will explain a list of 200+ Experimental Quantitative Research Topics For STEM Students.

And when it comes to writing a research title, we will guide you step by step. So, stay with us as we unlock the exciting world of STEM research – it is not just about grades; it is about growing smarter, more confident, and happier along the way.

What Is STEM?

Table of Contents

STEM is Science, Technology, Engineering, and Mathematics. It is a way of talking about things like learning, jobs, and activities related to these four important subjects. Science is about understanding the world around us, technology is about using tools and machines to solve problems, engineering is about designing and building things, and mathematics is about numbers and solving problems with them. STEM helps us explore, discover, and create cool stuff that makes our world better and more exciting.

Why STEM Research Is Important?

STEM research is important because it helps us learn new things about the world and solve problems. When scientists, engineers, and mathematicians study these subjects, they can discover cures for diseases, create new technology that makes life easier, and build things that help us live better. It is like a big puzzle where we put together pieces of knowledge to make our world safer, healthier, and more fun.

• STEM research leads to discoveries and solutions.
• It helps find cures for diseases.
• STEM technology makes life easier.
• Engineers build things that improve our lives.
• Mathematics helps us understand and solve complex problems. There are various Mathematic formulas that students should know.

How to Choose a Topic for STEM Research Paper

Here are some steps to choose a topic for STEM Research Paper:

Step 1: Identify Your Interests

Think about what you like and what excites you in science, technology, engineering, or math. It could be something you learned in school, saw in the news, or experienced in your daily life. Choosing a topic you’re passionate about makes the research process more enjoyable.

Step 2: Research Existing Topics

Look up different STEM research areas online, in books, or at your library. See what scientists and experts are studying. This can give you ideas and help you understand what’s already known in your chosen field.

Step 3: Consider Real-World Problems

Think about the problems you see around you. Are there issues in your community or the world that STEM can help solve? Choosing a topic that addresses a real-world problem can make your research impactful.

Step 4: Talk to Teachers and Mentors

Discuss your interests with your teachers, professors, or mentors. They can offer guidance and suggest topics that align with your skills and goals. They may also provide resources and support for your research.

Step 5: Narrow Down Your Topic

Once you have some ideas, narrow them down to a specific research question or project. Make sure it’s not too broad or too narrow. You want a topic that you can explore in depth within the scope of your research paper.

Here we will discuss 200+ Experimental Quantitative Research Topics For STEM Students: 

Qualitative Research Topics for STEM Students:

Qualitative research focuses on exploring and understanding phenomena through non-numerical data and subjective experiences. Here are 10 qualitative research topics for STEM students:

• Exploring the experiences of female STEM students in overcoming gender bias in academia.
• Understanding the perceptions of teachers regarding the integration of technology in STEM education.
• Investigating the motivations and challenges of STEM educators in underprivileged schools.
• Exploring the attitudes and beliefs of parents towards STEM education for their children.
• Analyzing the impact of collaborative learning on student engagement in STEM subjects.
• Investigating the experiences of STEM professionals in bridging the gap between academia and industry.
• Understanding the cultural factors influencing STEM career choices among minority students.
• Exploring the role of mentorship in the career development of STEM graduates.
• Analyzing the perceptions of students towards the ethics of emerging STEM technologies like AI and CRISPR. You may check the best AI tools like Top 10 AI Chatbots in 2024: Efficient ChatGPT Alternatives or Rise Of Generative AI: Transforming The Way Businesses Create Content .
• Investigating the emotional well-being and stress levels of STEM students during their academic journey.

Easy Experimental Research Topics for STEM Students:

These experimental research topics are relatively straightforward and suitable for STEM students who are new to research:

• Measuring the effect of different light wavelengths on plant growth.
• Investigating the relationship between exercise and heart rate in various age groups.
• Testing the effectiveness of different insulating materials in conserving heat.
• Examining the impact of pH levels on the rate of chemical reactions.
• Studying the behavior of magnets in different temperature conditions.
• Investigating the effect of different concentrations of a substance on bacterial growth.
• Testing the efficiency of various sunscreen brands in blocking UV radiation.
• Measuring the impact of music genres on concentration and productivity.
• Examining the correlation between the angle of a ramp and the speed of a rolling object.
• Investigating the relationship between the number of blades on a wind turbine and energy output.

Research Topics for STEM Students in the Philippines:

These research topics are tailored for STEM students in the Philippines:

• Assessing the impact of climate change on the biodiversity of coral reefs in the Philippines.
• Studying the potential of indigenous plants in the Philippines for medicinal purposes.
• Investigating the feasibility of harnessing renewable energy sources like solar and wind in rural Filipino communities.
• Analyzing the water quality and pollution levels in major rivers and lakes in the Philippines.
• Exploring sustainable agricultural practices for small-scale farmers in the Philippines.
• Assessing the prevalence and impact of dengue fever outbreaks in urban areas of the Philippines.
• Investigating the challenges and opportunities of STEM education in remote Filipino islands.
• Studying the impact of typhoons and natural disasters on infrastructure resilience in the Philippines.
• Analyzing the genetic diversity of endemic species in the Philippine rainforests.
• Assessing the effectiveness of disaster preparedness programs in Philippine communities.

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Good Research Topics for STEM Students:

These research topics are considered good because they offer interesting avenues for investigation and learning:

• Developing a low-cost and efficient water purification system for rural communities.
• Investigating the potential use of CRISPR-Cas9 for gene therapy in genetic disorders.
• Studying the applications of blockchain technology in securing medical records.
• Analyzing the impact of 3D printing on customized prosthetics for amputees.
• Exploring the use of artificial intelligence in predicting and preventing forest fires.
• Investigating the effects of microplastic pollution on aquatic ecosystems.
• Analyzing the use of drones in monitoring and managing crops.
• Studying the potential of quantum computing in solving complex optimization problems.
• Investigating the development of biodegradable materials for sustainable packaging.
• Exploring the ethical implications of gene editing in humans.

Unique Research Topics for STEM Students:

Unique research topics can provide STEM students with the opportunity to explore unconventional and innovative ideas. Here are 10 unique research topics for STEM students:

• Investigating the use of bioluminescent organisms for sustainable lighting solutions.
• Studying the potential of using spider silk proteins for advanced materials in engineering.
• Exploring the application of quantum entanglement for secure communication in the field of cryptography.
• Analyzing the feasibility of harnessing geothermal energy from underwater volcanoes.
• Investigating the use of CRISPR-Cas12 for rapid and cost-effective disease diagnostics.
• Studying the interaction between artificial intelligence and human creativity in art and music generation.
• Exploring the development of edible packaging materials to reduce plastic waste.
• Investigating the impact of microgravity on cellular behavior and tissue regeneration in space.
• Analyzing the potential of using sound waves to detect and combat invasive species in aquatic ecosystems.
• Studying the use of biotechnology in reviving extinct species, such as the woolly mammoth.

Experimental Research Topics for STEM Students in the Philippines

Research topics for STEM students in the Philippines can address specific regional challenges and opportunities. Here are 10 experimental research topics for STEM students in the Philippines:

• Assessing the effectiveness of locally sourced materials for disaster-resilient housing construction in typhoon-prone areas.
• Investigating the utilization of indigenous plants for natural remedies in Filipino traditional medicine.
• Studying the impact of volcanic soil on crop growth and agriculture in volcanic regions of the Philippines.
• Analyzing the water quality and purification methods in remote island communities.
• Exploring the feasibility of using bamboo as a sustainable construction material in the Philippines.
• Investigating the potential of using solar stills for freshwater production in water-scarce regions.
• Studying the effects of climate change on the migration patterns of bird species in the Philippines.
• Analyzing the growth and sustainability of coral reefs in marine protected areas.
• Investigating the utilization of coconut waste for biofuel production.
• Studying the biodiversity and conservation efforts in the Tubbataha Reefs Natural Park.

Capstone Research Topics for STEM Students in the Philippines:

Capstone research projects are often more comprehensive and can address real-world issues. Here are 10 capstone research topics for STEM students in the Philippines:

• Designing a low-cost and sustainable sanitation system for informal settlements in urban Manila.
• Developing a mobile app for monitoring and reporting natural disasters in the Philippines.
• Assessing the impact of climate change on the availability and quality of drinking water in Philippine cities.
• Designing an efficient traffic management system to address congestion in major Filipino cities.
• Analyzing the health implications of air pollution in densely populated urban areas of the Philippines.
• Developing a renewable energy microgrid for off-grid communities in the archipelago.
• Assessing the feasibility of using unmanned aerial vehicles (drones) for agricultural monitoring in rural Philippines.
• Designing a low-cost and sustainable aquaponics system for urban agriculture.
• Investigating the potential of vertical farming to address food security in densely populated urban areas.
• Developing a disaster-resilient housing prototype suitable for typhoon-prone regions.

Experimental Quantitative Research Topics for STEM Students:

Experimental quantitative research involves the collection and analysis of numerical data to conclude. Here are 10 Experimental Quantitative Research Topics For STEM Students interested in experimental quantitative research:

• Examining the impact of different fertilizers on crop yield in agriculture.
• Investigating the relationship between exercise and heart rate among different age groups.
• Analyzing the effect of varying light intensities on photosynthesis in plants.
• Studying the efficiency of various insulation materials in reducing building heat loss.
• Investigating the relationship between pH levels and the rate of corrosion in metals.
• Analyzing the impact of different concentrations of pollutants on aquatic ecosystems.
• Examining the effectiveness of different antibiotics on bacterial growth.
• Trying to figure out how temperature affects how thick liquids are.
• Finding out if there is a link between the amount of pollution in the air and lung illnesses in cities.
• Analyzing the efficiency of solar panels in converting sunlight into electricity under varying conditions.

Descriptive Research Topics for STEM Students

Descriptive research aims to provide a detailed account or description of a phenomenon. Here are 10 topics for STEM students interested in descriptive research:

• Describing the physical characteristics and behavior of a newly discovered species of marine life.
• Documenting the geological features and formations of a particular region.
• Creating a detailed inventory of plant species in a specific ecosystem.
• Describing the properties and behavior of a new synthetic polymer.
• Documenting the daily weather patterns and climate trends in a particular area.
• Providing a comprehensive analysis of the energy consumption patterns in a city.
• Describing the structural components and functions of a newly developed medical device.
• Documenting the characteristics and usage of traditional construction materials in a region.
• Providing a detailed account of the microbiome in a specific environmental niche.
• Describing the life cycle and behavior of a rare insect species.

Research Topics for STEM Students in the Pandemic:

The COVID-19 pandemic has raised many research opportunities for STEM students. Here are 10 research topics related to pandemics:

• Analyzing the effectiveness of various personal protective equipment (PPE) in preventing the spread of respiratory viruses.
• Studying the impact of lockdown measures on air quality and pollution levels in urban areas.
• Investigating the psychological effects of quarantine and social isolation on mental health.
• Analyzing the genomic variation of the SARS-CoV-2 virus and its implications for vaccine development.
• Studying the efficacy of different disinfection methods on various surfaces.
• Investigating the role of contact tracing apps in tracking & controlling the spread of infectious diseases.
• Analyzing the economic impact of the pandemic on different industries and sectors.
• Studying the effectiveness of remote learning in STEM education during lockdowns.
• Investigating the social disparities in healthcare access during a pandemic.
• Analyzing the ethical considerations surrounding vaccine distribution and prioritization.

Research Topics for STEM Students Middle School

Research topics for middle school STEM students should be engaging and suitable for their age group. Here are 10 research topics:

• Investigating the growth patterns of different types of mold on various food items.
• Studying the negative effects of music on plant growth and development.
• Analyzing the relationship between the shape of a paper airplane and its flight distance.
• Investigating the properties of different materials in making effective insulators for hot and cold beverages.
• Studying the effect of salt on the buoyancy of different objects in water.
• Analyzing the behavior of magnets when exposed to different temperatures.
• Investigating the factors that affect the rate of ice melting in different environments.
• Studying the impact of color on the absorption of heat by various surfaces.
• Analyzing the growth of crystals in different types of solutions.
• Investigating the effectiveness of different natural repellents against common pests like mosquitoes.

Technology Research Topics for STEM Students

Technology is at the forefront of STEM fields. Here are 10 research topics for STEM students interested in technology:

• Developing and optimizing algorithms for autonomous drone navigation in complex environments.
• Exploring the use of blockchain technology for enhancing the security and transparency of supply chains.
• Investigating the applications of virtual reality (VR) and augmented reality (AR) in medical training and surgery simulations.
• Studying the potential of 3D printing for creating personalized prosthetics and orthopedic implants.
• Analyzing the ethical and privacy implications of facial recognition technology in public spaces.
• Investigating the development of quantum computing algorithms for solving complex optimization problems.
• Explaining the use of machine learning and AI in predicting and mitigating the impact of natural disasters.
• Studying the advancement of brain-computer interfaces for assisting individuals with
• disabilities.
• Analyzing the role of wearable technology in monitoring and improving personal health and wellness.
• Investigating the use of robotics in disaster response and search and rescue operations.

Scientific Research Topics for STEM Students

Scientific research encompasses a wide range of topics. Here are 10 research topics for STEM students focusing on scientific exploration:

• Investigating the behavior of subatomic particles in high-energy particle accelerators.
• Studying the ecological impact of invasive species on native ecosystems.
• Analyzing the genetics of antibiotic resistance in bacteria and its implications for healthcare.
• Exploring the physics of gravitational waves and their detection through advanced interferometry.
• Investigating the neurobiology of memory formation and retention in the human brain.
• Studying the biodiversity and adaptation of extremophiles in harsh environments.
• Analyzing the chemistry of deep-sea hydrothermal vents and their potential for life beyond Earth.
• Exploring the properties of superconductors and their applications in technology.
• Investigating the mechanisms of stem cell differentiation for regenerative medicine.
• Studying the dynamics of climate change and its impact on global ecosystems.

Interesting Research Topics for STEM Students:

Engaging and intriguing research topics can foster a passion for STEM. Here are 10 interesting research topics for STEM students:

• Exploring the science behind the formation of auroras and their cultural significance.
• Investigating the mysteries of dark matter and dark energy in the universe.
• Studying the psychology of decision-making in high-pressure situations, such as sports or
• emergencies.
• Analyzing the impact of social media on interpersonal relationships and mental health.
• Exploring the potential for using genetic modification to create disease-resistant crops.
• Investigating the cognitive processes involved in solving complex puzzles and riddles.
• Studying the history and evolution of cryptography and encryption methods.
• Analyzing the physics of time travel and its theoretical possibilities.
• Exploring the role of Artificial Intelligence in creating art and music.
• Investigating the science of happiness and well-being, including factors contributing to life satisfaction.

Practical Research Topics for STEM Students

Practical research often leads to real-world solutions. Here are 10 practical research topics for STEM students:

• Developing an affordable and sustainable water purification system for rural communities.
• Designing a low-cost, energy-efficient home heating and cooling system.
• Investigating strategies for reducing food waste in the supply chain and households.
• Studying the effectiveness of eco-friendly pest control methods in agriculture.
• Analyzing the impact of renewable energy integration on the stability of power grids.
• Developing a smartphone app for early detection of common medical conditions.
• Investigating the feasibility of vertical farming for urban food production.
• Designing a system for recycling and upcycling electronic waste.
• Studying the environmental benefits of green roofs and their potential for urban heat island mitigation.
• Analyzing the efficiency of alternative transportation methods in reducing carbon emissions.

Experimental Research Topics for STEM Students About Plants

Plants offer a rich field for experimental research. Here are 10 experimental research topics about plants for STEM students:

• Investigating the effect of different light wavelengths on plant growth and photosynthesis.
• Studying the impact of various fertilizers and nutrient solutions on crop yield.
• Analyzing the response of plants to different types and concentrations of plant hormones.
• Investigating the role of mycorrhizal in enhancing nutrient uptake in plants.
• Studying the effects of drought stress and water scarcity on plant physiology and adaptation mechanisms.
• Analyzing the influence of soil pH on plant nutrient availability and growth.
• Investigating the chemical signaling and defense mechanisms of plants against herbivores.
• Studying the impact of environmental pollutants on plant health and genetic diversity.
• Analyzing the role of plant secondary metabolites in pharmaceutical and agricultural applications.
• Investigating the interactions between plants and beneficial microorganisms in the rhizosphere.

Qualitative Research Topics for STEM Students in the Philippines

Qualitative research in the Philippines can address local issues and cultural contexts. Here are 10 qualitative research topics for STEM students in the Philippines:

• Exploring indigenous knowledge and practices in sustainable agriculture in Filipino communities.
• Studying the perceptions and experiences of Filipino fishermen in coping with climate change impacts .
• Analyzing the cultural significance and traditional uses of medicinal plants in indigenous Filipino communities.
• Investigating the barriers and facilitators of STEM education access in remote Philippine islands.
• Exploring the role of traditional Filipino architecture in natural disaster resilience.
• Studying the impact of indigenous farming methods on soil conservation and fertility.
• Analyzing the cultural and environmental significance of mangroves in coastal Filipino regions.
• Investigating the knowledge and practices of Filipino healers in treating common ailments.
• Exploring the cultural heritage and conservation efforts of the Ifugao rice terraces.
• Studying the perceptions and practices of Filipino communities in preserving marine biodiversity.

Science Research Topics for STEM Students

Science offers a diverse range of research avenues. Here are 10 science research topics for STEM students:

• Investigating the potential of gene editing techniques like CRISPR-Cas9 in curing genetic diseases.
• Studying the ecological impacts of species reintroduction programs on local ecosystems.
• Analyzing the effects of microplastic pollution on aquatic food webs and ecosystems.
• Investigating the link between air pollution and respiratory health in urban populations.
• Studying the role of epigenetics in the inheritance of acquired traits in organisms.
• Analyzing the physiology and adaptations of extremophiles in extreme environments on Earth.
• Investigating the genetics of longevity and factors influencing human lifespan.
• Studying the behavioral ecology and communication strategies of social insects.
• Analyzing the effects of deforestation on global climate patterns and biodiversity loss.
• Investigating the potential of synthetic biology in creating bioengineered organisms for beneficial applications.

Correlational Research Topics for STEM Students

Correlational research focuses on relationships between variables. Here are 10 correlational research topics for STEM students:

• Analyzing the correlation between dietary habits and the incidence of chronic diseases.
• Studying the relationship between exercise frequency and mental health outcomes.
• Investigating the correlation between socioeconomic status and access to quality healthcare.
• Analyzing the link between social media usage and self-esteem in adolescents.
• Studying the correlation between academic performance and sleep duration among students.
• Investigating the relationship between environmental factors and the prevalence of allergies.
• Analyzing the correlation between technology use and attention span in children.
• Studying how environmental factors are related to the frequency of allergies.
• Investigating the link between parental involvement in education and student achievement.
• Analyzing the correlation between temperature fluctuations and wildlife migration patterns.

Quantitative Research Topics for STEM Students in the Philippines

Quantitative research in the Philippines can address specific regional issues. Here are 10 quantitative research topics for STEM students in the Philippines

• Analyzing the impact of typhoons on coastal erosion rates in the Philippines.
• Studying the quantitative effects of land use change on watershed hydrology in Filipino regions.
• Investigating the quantitative relationship between deforestation and habitat loss for endangered species.
• Analyzing the quantitative patterns of marine biodiversity in Philippine coral reef ecosystems.
• Studying the quantitative assessment of water quality in major Philippine rivers and lakes.
• Investigating the quantitative analysis of renewable energy potential in specific Philippine provinces.
• Analyzing the quantitative impacts of agricultural practices on soil health and fertility.
• Studying the quantitative effectiveness of mangrove restoration in coastal protection in the Philippines.
• Investigating the quantitative evaluation of indigenous agricultural practices for sustainability .
• Analyzing the quantitative patterns of air pollution and its health impacts in urban Filipino areas.

Things That Must Keep In Mind While Writing Quantitative Research Title 

Here are a few things that must be kept in mind while writing a quantitative research:

1. Be Clear and Precise

Make sure your research title is clear and says exactly what your study is about. People should easily understand the topic and goals of your research by reading the title.

2. Use Important Words

Include words that are crucial to your research, like the main subjects, who you’re studying, and how you’re doing your research. This helps others find your work and understand what it’s about.

3. Avoid Confusing Words

Stay away from words that might confuse people. Your title should be easy to grasp, even if someone isn’t an expert in your field.

4. Show Your Research Approach

Tell readers what kind of research you did, like experiments or surveys. This gives them a hint about how you conducted your study.

5. Match Your Title with Your Research Questions

Make sure your title matches the questions you’re trying to answer in your research. It should give a sneak peek into what your study is all about and keep you on the right track as you work on it.

STEM students, addressing what STEM is and why research matters in this field. It offered an extensive list of research topics , including experimental, qualitative, and regional options, catering to various academic levels and interests. Whether you’re a middle school student or pursuing advanced studies, these topics offer a wealth of ideas. The key takeaway is to choose a topic that resonates with your passion and aligns with your goals, ensuring a successful journey in STEM research. Choose the best Experimental Quantitative Research Topics For STEM students today!

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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10 Experimental research

Experimental research—often considered to be the ‘gold standard’ in research designs—is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research—rather than for descriptive or exploratory research—where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalisability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments are conducted in field settings such as in a real organisation, and are high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favourably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receiving a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the ‘cause’ in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and ensures that each unit in the population has a positive chance of being selected into the sample. Random assignment, however, is a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group prior to treatment administration. Random selection is related to sampling, and is therefore more closely related to the external validity (generalisability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.

Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.

Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam.

Not conducting a pretest can help avoid this threat.

Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.

Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.

Regression threat —also called a regression to the mean—refers to the statistical tendency of a group’s overall performance to regress toward the mean during a posttest rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest were possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-group experimental designs

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest-posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement—especially if the pretest introduces unusual topics or content.

Posttest -only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

The appropriate statistical analysis of this design is also a two-group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Due to the presence of covariates, the right statistical analysis of this design is a two-group analysis of covariance (ANCOVA). This design has all the advantages of posttest-only design, but with internal validity due to the controlling of covariates. Covariance designs can also be extended to pretest-posttest control group design.

Factorial designs

Two-group designs are inadequate if your research requires manipulation of two or more independent variables (treatments). In such cases, you would need four or higher-group designs. Such designs, quite popular in experimental research, are commonly called factorial designs. Each independent variable in this design is called a factor , and each subdivision of a factor is called a level . Factorial designs enable the researcher to examine not only the individual effect of each treatment on the dependent variables (called main effects), but also their joint effect (called interaction effects).

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for three hours/week of instructional time than for one and a half hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid experimental designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomised bocks design, Solomon four-group design, and switched replications design.

Randomised block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full-time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between the treatment group (receiving the same treatment) and the control group (see Figure 10.5). The purpose of this design is to reduce the ‘noise’ or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs, but not in posttest-only designs. The design notation is shown in Figure 10.6.

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organisational contexts where organisational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Quasi-experimental designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organisation is used as the treatment group, while another section of the same class or a different organisation in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impacted by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

In addition, there are quite a few unique non-equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to the treatment or control group based on a cut-off score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardised test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program.

Because of the use of a cut-off score, it is possible that the observed results may be a function of the cut-off score rather than the treatment, which introduces a new threat to internal validity. However, using the cut-off score also ensures that limited or costly resources are distributed to people who need them the most, rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design do not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, say you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data is not available from the same subjects.

An interesting variation of the NEDV design is a pattern-matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique—based on the degree of correspondence between theoretical and observed patterns—is a powerful way of alleviating internal validity concerns in the original NEDV design.

Perils of experimental research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, often experimental research uses inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies, and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artefact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if in doubt, use tasks that are simple and familiar for the respondent sample rather than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

Social Science Research: Principles, Methods and Practices (Revised edition) Copyright © 2019 by Anol Bhattacherjee is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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10 Real-Life Experimental Research Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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Experimental research is research that involves using a scientific approach to examine research variables.

Below are some famous experimental research examples. Some of these studies were conducted quite a long time ago. Some were so controversial that they would never be attempted today. And some were so unethical that they would never be permitted again.

A few of these studies have also had very practical implications for modern society involving criminal investigations, the impact of television and the media, and the power of authority figures.

Examples of Experimental Research

1. pavlov’s dog: classical conditioning.

Dr. Ivan Pavlov was a physiologist studying animal digestive systems in the 1890s. In one study, he presented food to a dog and then collected its salivatory juices via a tube attached to the inside of the animal’s mouth.

As he was conducting his experiments, an annoying thing kept happening; every time his assistant would enter the lab with a bowl of food for the experiment, the dog would start to salivate at the sound of the assistant’s footsteps.

Although this disrupted his experimental procedures, eventually, it dawned on Pavlov that something else was to be learned from this problem.

Pavlov learned that animals could be conditioned into responding on a physiological level to various stimuli, such as food, or even the sound of the assistant bringing the food down the hall.

Hence, the creation of the theory of classical conditioning. One of the most influential theories in psychology still to this day.

2. Bobo Doll Experiment: Observational Learning

Dr. Albert Bandura conducted one of the most influential studies in psychology in the 1960s at Stanford University.

His intention was to demonstrate that cognitive processes play a fundamental role in learning. At the time, Behaviorism was the predominant theoretical perspective, which completely rejected all inferences to constructs not directly observable .

So, Bandura made two versions of a video. In version #1, an adult behaved aggressively with a Bobo doll by throwing it around the room and striking it with a wooden mallet. In version #2, the adult played gently with the doll by carrying it around to different parts of the room and pushing it gently.

After showing children one of the two versions, they were taken individually to a room that had a Bobo doll. Their behavior was observed and the results indicated that children that watched version #1 of the video were far more aggressive than those that watched version #2.

Not only did Bandura’s Bobo doll study form the basis of his social learning theory, it also helped start the long-lasting debate about the harmful effects of television on children.

Worth Checking Out: What’s the Difference between Experimental and Observational Studies?

3. The Asch Study: Conformity  

Dr. Solomon Asch was interested in conformity and the power of group pressure. His study was quite simple. Different groups of students were shown lines of varying lengths and asked, “which line is longest.”

However, out of each group, only one was an actual participant. All of the others in the group were working with Asch and instructed to say that one of the shorter lines was actually the longest.

Nearly every time, the real participant gave an answer that was clearly wrong, but the same as the rest of the group.

The study is one of the most famous in psychology because it demonstrated the power of social pressure so clearly.  

4. Car Crash Experiment: Leading Questions

In 1974, Dr. Elizabeth Loftus and her undergraduate student John Palmer designed a study to examine how fallible human judgement is under certain conditions.

They showed groups of research participants videos that depicted accidents between two cars. Later, the participants were asked to estimate the rate of speed of the cars.

Here’s the interesting part. All participants were asked the same question with the exception of a single word: “How fast were the two cars going when they ______into each other?” The word in the blank varied in its implied severity.

Participants’ estimates were completely affected by the word in the blank. When the word “smashed” was used, participants estimated the cars were going much faster than when the word “contacted” was used. 

This line of research has had a huge impact on law enforcement interrogation practices, line-up procedures, and the credibility of eyewitness testimony .

5. The 6 Universal Emotions

The research by Dr. Paul Ekman has been influential in the study of emotions. His early research revealed that all human beings, regardless of culture, experience the same 6 basic emotions: happiness, sadness, disgust, fear, surprise, and anger.

In the late 1960s, Ekman traveled to Papua New Guinea. He approached a tribe of people that were extremely isolated from modern culture. With the help of a guide, he would describe different situations to individual members and take a photo of their facial expressions.

The situations included: if a good friend had come; their child had just died; they were about to get into a fight; or had just stepped on a dead pig.

The facial expressions of this highly isolated tribe were nearly identical to those displayed by people in his studies in California.

6. The Little Albert Study: Development of Phobias  

Dr. John Watson and Dr. Rosalie Rayner sought to demonstrate how irrational fears were developed.

Their study involved showing a white rat to an infant. Initially, the child had no fear of the rat. However, the researchers then began to create a loud noise each time they showed the child the rat by striking a steel bar with a hammer.

Eventually, the child started to cry and feared the white rat. The child also developed a fear of other white, furry objects such as white rabbits and a Santa’s beard.

This study is famous because it demonstrated one way in which phobias are developed in humans, and also because it is now considered highly unethical for its mistreatment of children, lack of study debriefing , and intent to instil fear.  

7. A Class Divided: Discrimination

Perhaps one of the most famous psychological experiments of all time was not conducted by a psychologist. In 1968, third grade teacher Jane Elliott conducted one of the most famous studies on discrimination in history. It took place shortly after the assassination of Dr. Martin Luther King, Jr.

She divided her class into two groups: brown-eyed and blue-eyed students. On the first day of the experiment, she announced the blue-eyed group as superior. They received extra privileges and were told not to intermingle with the brown-eyed students.

They instantly became happier, more self-confident, and started performing better academically.

The next day, the roles were reversed. The brown-eyed students were announced as superior and given extra privileges. Their behavior changed almost immediately and exhibited the same patterns as the other group had the day before.

This study was a remarkable demonstration of the harmful effects of discrimination.

8. The Milgram Study: Obedience to Authority

Dr. Stanley Milgram conducted one of the most influential experiments on authority and obedience in 1961 at Yale University.

Participants were told they were helping study the effects of punishment on learning. Their job was to administer an electric shock to another participant each time they made an error on a test. The other participant was actually an actor in another room that only pretended to be shocked.

However, each time a mistake was made, the level of shock was supposed to increase, eventually reaching quite high voltage levels. When the real participants expressed reluctance to administer the next level of shock, the experimenter, who served as the authority figure in the room, pressured the participant to deliver the next level of shock.

The results of this study were truly astounding. A surprisingly high percentage of participants continued to deliver the shocks to the highest level possible despite the very strong objections by the “other participant.”

This study demonstrated the power of authority figures.

9. The Marshmallow Test: Delay of Gratification

The Marshmallow Test was designed by Dr. Walter Mischel to examine the role of delay of gratification and academic success.

Children ages 4-6 years old were seated at a table with one marshmallow placed in front of them. The experimenter explained that if they did not eat the marshmallow, they would receive a second one. They could then eat both.

The children that were able to delay gratification the longest were rated as significantly more competent later in life and earned higher SAT scores than children that could not withstand the temptation.  

The study has since been conceptually replicated by other researchers that have revealed additional factors involved in delay of gratification and academic achievement.

10. Stanford Prison Study: Deindividuation

Dr. Philip Zimbardo conducted one of the most famous psychological studies of all time in 1971. The purpose of the study was to investigate how the power structure in some situations can lead people to behave in ways highly uncharacteristic of their usual behavior.

College students were recruited to participate in the study. Some were randomly assigned to play the role of prison guard. The others were actually “arrested” by real police officers. They were blindfolded and taken to the basement of the university’s psychology building which had been converted to look like a prison.

Although the study was supposed to last 2 weeks, it had to be halted due to the abusive actions of the guards.

The study demonstrated that people will behave in ways they never thought possible when placed in certain roles and power structures. Although the Stanford Prison Study is so well-known for what it revealed about human nature, it is also famous because of the numerous violations of ethical principles.

The studies above are varied and focused on many different aspects of human behavior . However, each example of experimental research listed above has had a lasting impact on society. Some have had tremendous sway in how very practical matters are conducted, such as criminal investigations and legal proceedings.

Psychology is a field of study that is often not fully understood by the general public. When most people hear the term “psychology,” they think of a therapist that listens carefully to the revealing statements of a patient. The therapist then tries to help their patient learn to cope with many of life’s challenges. Nothing wrong with that.

In reality however, most psychologists are researchers. They spend most of their time designing and conducting experiments to enhance our understanding of the human condition.

Asch SE. (1956). Studies of independence and conformity: I. A minority of one against a unanimous majority . Psychological Monographs: General and Applied, 70 (9),1-70. https://doi.org/doi:10.1037/h0093718

Bandura A. (1965). Influence of models’ reinforcement contingencies on the acquisition of imitative responses. Journal of Personality and Social Psychology, 1 (6), 589-595. https://doi.org/doi:10.1037/h0022070

Beck, H. P., Levinson, S., & Irons, G. (2009). Finding little Albert: A journey to John B. Watson’s infant laboratory.  American Psychologist, 64(7),  605-614.

Ekman, P. & Friesen, W. V. (1971).  Constants Across Cultures in the Face and motion .  Journal of Personality and Social Psychology, 17(2) , 124-129.

Loftus, E. F., & Palmer, J. C. (1974). Reconstruction of automobile destruction: An example of

the interaction between language and memory. Journal of Verbal Learning and Verbal

Behavior, 13 (5), 585–589.

Milgram S (1965). Some Conditions of Obedience and Disobedience to Authority. Human Relations, 18(1), 57–76.

Mischel, W., & Ebbesen, E. B. (1970). Attention in delay of gratification . Journal of Personality and Social Psychology, 16 (2), 329-337.

Pavlov, I.P. (1927). Conditioned Reflexes . London: Oxford University Press.

Watson, J. & Rayner, R. (1920). Conditioned emotional reactions.  Journal of Experimental Psychology, 3 , 1-14. Zimbardo, P., Haney, C., Banks, W. C., & Jaffe, D. (1971). The Stanford Prison Experiment: A simulation study of the psychology of imprisonment . Stanford University, Stanford Digital Repository, Stanford.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
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Experimental design: Guide, steps, examples

Last updated

27 April 2023

Reviewed by

Miroslav Damyanov

Short on time? Get an AI generated summary of this article instead

Experimental research design is a scientific framework that allows you to manipulate one or more variables while controlling the test environment. 

When testing a theory or new product, it can be helpful to have a certain level of control and manipulate variables to discover different outcomes. You can use these experiments to determine cause and effect or study variable associations. 

This guide explores the types of experimental design, the steps in designing an experiment, and the advantages and limitations of experimental design. 

Make research less tedious

Dovetail streamlines research to help you uncover and share actionable insights

• What is experimental research design?

You can determine the relationship between each of the variables by: 

Manipulating one or more independent variables (i.e., stimuli or treatments)

Applying the changes to one or more dependent variables (i.e., test groups or outcomes)

With the ability to analyze the relationship between variables and using measurable data, you can increase the accuracy of the result. 

What is a good experimental design?

A good experimental design requires: 

Significant planning to ensure control over the testing environment

Sound experimental treatments

Properly assigning subjects to treatment groups

Without proper planning, unexpected external variables can alter an experiment's outcome. 

To meet your research goals, your experimental design should include these characteristics:

Provide unbiased estimates of inputs and associated uncertainties

Enable the researcher to detect differences caused by independent variables

Include a plan for analysis and reporting of the results

Provide easily interpretable results with specific conclusions

What's the difference between experimental and quasi-experimental design?

The major difference between experimental and quasi-experimental design is the random assignment of subjects to groups. 

A true experiment relies on certain controls. Typically, the researcher designs the treatment and randomly assigns subjects to control and treatment groups. 

However, these conditions are unethical or impossible to achieve in some situations.

When it's unethical or impractical to assign participants randomly, that’s when a quasi-experimental design comes in. 

This design allows researchers to conduct a similar experiment by assigning subjects to groups based on non-random criteria. 

Another type of quasi-experimental design might occur when the researcher doesn't have control over the treatment but studies pre-existing groups after they receive different treatments.

When can a researcher conduct experimental research?

Various settings and professions can use experimental research to gather information and observe behavior in controlled settings. 

Basically, a researcher can conduct experimental research any time they want to test a theory with variable and dependent controls. 

Experimental research is an option when the project includes an independent variable and a desire to understand the relationship between cause and effect. 

• The importance of experimental research design

Experimental research enables researchers to conduct studies that provide specific, definitive answers to questions and hypotheses. 

Researchers can test Independent variables in controlled settings to:

Test the effectiveness of a new medication

Design better products for consumers

Answer questions about human health and behavior

Developing a quality research plan means a researcher can accurately answer vital research questions with minimal error. As a result, definitive conclusions can influence the future of the independent variable. 

Types of experimental research designs

There are three main types of experimental research design. The research type you use will depend on the criteria of your experiment, your research budget, and environmental limitations. 

Pre-experimental research design

A pre-experimental research study is a basic observational study that monitors independent variables’ effects. 

During research, you observe one or more groups after applying a treatment to test whether the treatment causes any change. 

The three subtypes of pre-experimental research design are:

One-shot case study research design

This research method introduces a single test group to a single stimulus to study the results at the end of the application. 

After researchers presume the stimulus or treatment has caused changes, they gather results to determine how it affects the test subjects. 

One-group pretest-posttest design

This method uses a single test group but includes a pretest study as a benchmark. The researcher applies a test before and after the group’s exposure to a specific stimulus. 

Static group comparison design

This method includes two or more groups, enabling the researcher to use one group as a control. They apply a stimulus to one group and leave the other group static. 

A posttest study compares the results among groups. 

True experimental research design

A true experiment is the most common research method. It involves statistical analysis to prove or disprove a specific hypothesis . 

Under completely experimental conditions, researchers expose participants in two or more randomized groups to different stimuli. 

Random selection removes any potential for bias, providing more reliable results. 

These are the three main sub-groups of true experimental research design:

Posttest-only control group design

This structure requires the researcher to divide participants into two random groups. One group receives no stimuli and acts as a control while the other group experiences stimuli.

Researchers perform a test at the end of the experiment to observe the stimuli exposure results.

Pretest-posttest control group design

This test also requires two groups. It includes a pretest as a benchmark before introducing the stimulus. 

The pretest introduces multiple ways to test subjects. For instance, if the control group also experiences a change, it reveals that taking the test twice changes the results.

Solomon four-group design

This structure divides subjects into two groups, with two as control groups. Researchers assign the first control group a posttest only and the second control group a pretest and a posttest. 

The two variable groups mirror the control groups, but researchers expose them to stimuli. The ability to differentiate between groups in multiple ways provides researchers with more testing approaches for data-based conclusions. 

Quasi-experimental research design

Although closely related to a true experiment, quasi-experimental research design differs in approach and scope. 

Quasi-experimental research design doesn’t have randomly selected participants. Researchers typically divide the groups in this research by pre-existing differences. 

Quasi-experimental research is more common in educational studies, nursing, or other research projects where it's not ethical or practical to use randomized subject groups.

• 5 steps for designing an experiment

Experimental research requires a clearly defined plan to outline the research parameters and expected goals. 

Here are five key steps in designing a successful experiment:

Step 1: Define variables and their relationship

Your experiment should begin with a question: What are you hoping to learn through your experiment? 

The relationship between variables in your study will determine your answer.

Define the independent variable (the intended stimuli) and the dependent variable (the expected effect of the stimuli). After identifying these groups, consider how you might control them in your experiment. 

Could natural variations affect your research? If so, your experiment should include a pretest and posttest. 

Step 2: Develop a specific, testable hypothesis

With a firm understanding of the system you intend to study, you can write a specific, testable hypothesis. 

What is the expected outcome of your study? 

Develop a prediction about how the independent variable will affect the dependent variable. 

How will the stimuli in your experiment affect your test subjects? 

Your hypothesis should provide a prediction of the answer to your research question . 

Step 3: Design experimental treatments to manipulate your independent variable

Depending on your experiment, your variable may be a fixed stimulus (like a medical treatment) or a variable stimulus (like a period during which an activity occurs). 

Determine which type of stimulus meets your experiment’s needs and how widely or finely to vary your stimuli. 

Step 4: Assign subjects to groups

When you have a clear idea of how to carry out your experiment, you can determine how to assemble test groups for an accurate study. 

When choosing your study groups, consider: 

The size of your experiment

Whether you can select groups randomly

Your target audience for the outcome of the study

You should be able to create groups with an equal number of subjects and include subjects that match your target audience. Remember, you should assign one group as a control and use one or more groups to study the effects of variables. 

Step 5: Plan how to measure your dependent variable

This step determines how you'll collect data to determine the study's outcome. You should seek reliable and valid measurements that minimize research bias or error. 

You can measure some data with scientific tools, while you’ll need to operationalize other forms to turn them into measurable observations.

• Advantages of experimental research

Experimental research is an integral part of our world. It allows researchers to conduct experiments that answer specific questions. 

While researchers use many methods to conduct different experiments, experimental research offers these distinct benefits:

Researchers can determine cause and effect by manipulating variables.

It gives researchers a high level of control.

Researchers can test multiple variables within a single experiment.

All industries and fields of knowledge can use it. 

Researchers can duplicate results to promote the validity of the study .

Replicating natural settings rapidly means immediate research.

Researchers can combine it with other research methods.

It provides specific conclusions about the validity of a product, theory, or idea.

• Disadvantages (or limitations) of experimental research

Unfortunately, no research type yields ideal conditions or perfect results. 

While experimental research might be the right choice for some studies, certain conditions could render experiments useless or even dangerous. 

Before conducting experimental research, consider these disadvantages and limitations:

Required professional qualification

Only competent professionals with an academic degree and specific training are qualified to conduct rigorous experimental research. This ensures results are unbiased and valid. 

Limited scope

Experimental research may not capture the complexity of some phenomena, such as social interactions or cultural norms. These are difficult to control in a laboratory setting.

Resource-intensive

Experimental research can be expensive, time-consuming, and require significant resources, such as specialized equipment or trained personnel.

Limited generalizability

The controlled nature means the research findings may not fully apply to real-world situations or people outside the experimental setting.

Practical or ethical concerns

Some experiments may involve manipulating variables that could harm participants or violate ethical guidelines . 

Researchers must ensure their experiments do not cause harm or discomfort to participants. 

Sometimes, recruiting a sample of people to randomly assign may be difficult. 

• Experimental research design example

Experiments across all industries and research realms provide scientists, developers, and other researchers with definitive answers. These experiments can solve problems, create inventions, and heal illnesses. 

Product design testing is an excellent example of experimental research. 

A company in the product development phase creates multiple prototypes for testing. With a randomized selection, researchers introduce each test group to a different prototype. 

When groups experience different product designs , the company can assess which option most appeals to potential customers. 

Experimental research design provides researchers with a controlled environment to conduct experiments that evaluate cause and effect. 

Using the five steps to develop a research plan ensures you anticipate and eliminate external variables while answering life’s crucial questions.

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146 Experiment Research Topics

Welcome to our collection of experimental research topics! Experiments are the cornerstone of empirical research, allowing scholars to test hypotheses and expand knowledge. With our experimental research questions ideas, you can uncover the diverse realms of empirical studies, from the natural sciences to social sciences and beyond.

🧪 7 Best Experimental Research Questions Ideas

🏆 best experimental research topics, 💡 simple experimental essay titles, 👍 catchy experimental research questions ideas, ❓ more experimental research questions ideas, 🎓 interesting experimental research topics.

• Bean Seed Germination Experiment Results
• Scientific Report Draft on Osmosis Egg Experiment
• Motor Speed and Input Characteristics Experiment
• Water Quality and Contamination Experiment Report
• Archimedes’ Principle Experiment: Determining Gravity of Objects
• Static and Kinetic Friction: A Lab Experiment
• “Stanford Prison Experiment Ethics” by Philip Zimbardo
• Physical Health Indicator: Pulse Rate Experiment An examination of a person’s pulse can provide insight into their health, especially when measuring the before and after the pulse of an individual engaged in exercise.
• Inductor-Capacitor-Resistor Circuit Experiment The article presents the experiment that will demonstrate the relationship between an inductor, voltmeter, and resistor in an inductor-capacitor-resistor (LCR) circuit.
• Helicopter Experiment Assessment This report of a paper helicopter experiment involved designating a paper helicopter in varied designs and then dropping it severally while recording the flight time.
• Miles Davis and Steve Reich: Geniuses of Experiments and Creativity Although Miles Davis’ and Steve Reich’s music belongs to different genres, they are connected in their constant search for a new sound by experimenting and improvising.
• Metal and Non-metal Redox Reactions Experiment The following experiment aimed to investigate Redox reaction and hence determine which elements were reactive; metal v. metal redox reactions, and non-metal v. non-metal reactions.
• Fiji Water Quality: Biology Lab Experiment Since Fiji water is among the popular brands in the US, it is essential to evaluate whether it is clean, that is, safe for human consumption.
• John Watson and the “Little Albert” Experiment John Watson is considered to be the founder of behaviorism, a psychological theory that focuses on visible behavior while diminishing the notion of consciousness.
• Hawthorne Experiments – Elton Mayo With Roethlisberger and Dickson The Hawthorne theories have brought about a positive change in the behavior and attitude of the managers as well as the workers.
• Unethical Research Experiments Violation of ethical principles can be traced in two analyzed cases; only in Landis’s experiment harm and killing were real in relation to animals.
• Human Transport Systems: The Pulse Rate Experiment The report provides an analysis of the pulse rate experiment aimed at determining the pulse rates before and after a five-minute exercise conducted by the researcher.
• Extraneous Variables in Experiments There are some variables in experiments besides the independent variables that usually cause a variation or a change to the dependent variables.
• Putnam’s “Twin Earth” Thought-Experiment Throughout the history of analytic philosophy, the problem of meaning has been and remains one of its central themes.
• Metrology Experiment with Measurement Tools The experiment concerned testing the efficacy of the measurement tools such as the Vernier caliper, a depth gauge, a micrometer, and a gauge in an uncertainty analysis.
• The Stanford Prison Experiment The Stanford prison experiment is an example of how outside social situations influence changes in thought and behavior among humans.
• The Marshmallow Experiment Articles The two works, “Don’t Eat the Marshmallow” by Joachim de Posada and “Why Rich Kids Are So Good at the Marshmallow Test” by Anindya Kundu, both focus on the marshmallow experiment.
• Experiments in High-Frequency Trading High-frequency trading (HFT) is becoming increasingly popular with private businesses and traders. HFT allows traders to make transactions within fractions of seconds.
• P. Zimbardo’s Stanford Experiment A psychological experiment is an event conducted to acquire new scientific knowledge about psychology through the researcher’s deliberate intervention in the life of the examinee.
• Kant’s Ethical Philosophy and Milgram’s Experiments The problem for Kant’s ethical philosophy is whether moral principles are applicable to nonhumans, such as Galacticans.
• A Personal Behavior Modification Experiment Using Operant Conditioning This research paper points out the positive outcomes of swearing: it can relieve stress and help one cope with emotional work.
• Air Pressure Experiment Methods and Results The plastic mesh fabric was placed over the mouth of the Mason jar, and the metal screw band of the latter was fastened firmly over the plastic mesh sheet.
• Virtue Ethics in Stanford and Milgram’s Experiments This paper investigates the notion of virtue ethics, discussing two major studies, the Stanford prison experiment, and Milgram’s obedience studies.
• Conducting a Titration Experiment Titration studies are conducted to quantify the amount of an unidentified element in the sample using a methodological approach.
• Experiment on Effect of Energy Drinks on Athletic Performance Experimental research is a study that a researcher sets up to evaluate a given situation, such as a drug or treatment intervention.
• Experiment: Flame Test and Chemical Fingerprinting Flame test and chemical fingerprinting are analytical procedures that are used to identify metals or metalloid compounds.
• The Use of Animals in Psychological Experiments The method of experimentation is of great significance for multiple fields of psychology, especially for the behaviorist branch.
• Why People Obey Authority: Milgram Experiment and Real-World Situation Human beings would obey authority depending on the overall rewards, potential personal gains, and the consequences of failing to do so.
• Ideal Gas Expansion Law: Experiment The purpose of the experiment was to understand the differences between different types of ideal gas expansions, paying attention to the amount of work done.
• Acoustics Experiment in Brunel’s Thames Tunnel In this project, tunnels that exist below London streets for a variety of communications, civil defense, and military purposes will be used as the objects of the experiment.
• Osmosis Experiment With Parsnip Through Differing Concentration of Sucrose
• Identifying the Benefits of Home Ownership: A Swedish Experiment
• Experiment for Cancer Risk Factors
• Hydrochloric Acid Into Tubes of Water and Sodium Thiosulphate Experiment
• General Information about Monkey Drug Trials Experiment
• Reaction Rates Experiment Hydrochloric Acid
• Hydrochloric Acid and Marble Chips Experiment
• Physical Disability and Labor Market Discrimination: Evidence From a Field Experiment
• Canadian Advanced Nanosatellite Experiment Biology
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• Experiment and Multi-Grid Modeling of Evacuation From a Classroom
• High-Performance Liquid Chromatography Experiment
• Social Capital and Contributions in a Public-Goods Experiment
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• Short Selling and Earnings Management: A Controlled Experiment
• Theft and Rural Poverty: Results of a Natural Experiment
• Lab Experiment: The Effectiveness of Different Antibiotics on Bacteria
• Brucellosis and Its Treatment: Experiment With Doxycycline
• The Link Between Stanford Prison Experiment and Milgram Study
• Four Fundamental Results From the Mice Experiment
• An Observable Experiment: Control Over the Variables An observable experiment is defined as the experiment in which the independent variables cannot possibly be controlled by the person or person setting the test.
• Stanford Prison Experiment: Behind the Mask Stanford Prison Experiment organized by Stanford researcher Philip Zimbardo led to a strong public response and still discussed today.
• Ethical Analysis of the Tuskegee Syphilis Experiments The Tuskegee Syphilis Study failed to take into account several critical ethical considerations. This essay examines some of the ethical problems linked to the investigation.
• The Stanford Prison Experiment Review The video presents an experiment held in 1971. In general, a viewer can observe that people are subjected to behavior and opinion change when affected by others.
• The Importance of Safety in Chemical Experiments Chemical experiments can teach students a lot and show new unknown properties of substances. To protect oneself and others, it is crucial to adhere to rules.
• The Stanford Prison Experiment Analysis Abuse between guards and prisoners is an imminent factor attributed to the differential margin on duties and responsibilities.
• The Stanford Prison Experiment’s Historical Record The Stanford Prison Experiment is a seminal investigation into the dynamics of peer pressure in human psychology.
• Socioeconomic Status and Sentencing Severity Experiment There are two types of validity threats: external and internal. External validity refers to the degree to which the study can be applied to situations outside the research context.
• Psychology: Zimbardo Prison Experiment Despite all the horrors that contradict ethics, Zimbardo’s research contributed to the formation of social psychology. It was unethical to conduct this experiment.
• Post-Covid Adaptation Laboratory Experiment The goal of the laboratory experiment that this paper will outline is to test the hypothesis about the needs of senior citizens in the post-pandemic era.
• Psychology: Milgram Obedience Experiment Milgram’s experiment may be the last psychological experiment that has had a significant impact on psychology and public opinion.
• Predicting the Replicability of Social Science Lab Experiments The quality of work is the most significant factor for any academic organization. A research process for any scientific project requires careful evaluation of information sources.
• Moral Dilemma and Thought Experiments The aim of this essay is to set up a thought experiment in which a moral dilemma must be resolved. A person is invited to make a choice as a result of which people should suffer.
• The Ethical Issues in 1940’s U.S. Experiments With Syphilis in Guatemala The Guatemala tests have been viewed as a dark side of the U.S. clinical examination: in the 1940s, they purposely uncovered over 5,000 individuals with syphilis and gonorrhea.
• Isopods and Their Use in Experiments Isopod is a large family belonging to the crayfish order. The fact that isopods are good to use in various experiments is related to their habitat.
• Sociological Experiment: The Salience of Social Norms Based on the sociological experiment described in the paper, the author demonstrated the salience of social norms that exist in our culture.
• Thought Experiment: The Morality of Human Actions A thought experiment aimed at assessing the morality of human actions motivated by divine punishment or reward raises the question of morality and religion correlation.
• Ethical Implications of the Early Studies in Psychology: Milgram’s Experiment Milgram’s experiment on obedience content and results are valuable for understanding the ethical issues that may occur in social and behavioral research.
• Blue-Eyed vs. Brown-Eyed Experiment Elliot exposed the learners to discrimination, in which blue-eyed children were initially preferred and given more privileges in the classroom than brown-eyed students.
• Experiment: Science Meets Real Life The experiment involves the sequential study of the dog’s behavior and its reaction to a change in some factors, such as food and bowl.
• Should Animals Be Used for Scientific Experiments? Unfortunately, at the moment, the use of animals in science and medicine cannot be excluded entirely. However, it is possible to conduct experiments using mathematical models.
• Smoking: An Idea for a Statistical Experiment The hypothesis is that people who smoke cigarettes daily tend to earn more than others: this is a personal observation that requires careful experimental testing.
• The Stanford Jail Experiment Critiques One of the most important critiques leveled at the Stanford Jail Experiment is the length of time it took Zimbardo to call a halt to the experiment.
• Super Size Me and Jogn Cisna Experiments In comparison to Super Size Me, the experiment of John Cisna immediately stands out with a positive attitude towards fast food.
• The Milgram Experiment: Ethical Issues The Milgram experiment is a controversial study on the subject of obedience to authority figures. The participants were asked to deliver electric shocks to other people.
• Health and Medicine: Experiments and Discussions In the first experiment, researchers tested the subjectivity of polygraph examiners’ assessments. The specialist was given a specific name before the test began to do it.
• “Tuskegee Syphilis Experiment – The Deadly Deception”: Unethical Scientific Experiment “Tuskegee Syphilis Experiment – The Deadly Deception” reviews an unethical scientific experiment on humans that was conducted by White physicians on African-Americans.
• An Experiment in DNA Cloning and Sequencing The aim of this experiment is to clone a fragment of DNA that includes the Green Fluorescent Protein (GFP) gene into the vector pTTQ18, which is an expression vector.
• Lab Experiment on Animals’ Taste or Smell Senses The hypothesis of the study is that taste perception and detection of different sugars by insects were similar to that of humans.
• Triacylglycerols: Definition and Extraction Experiment The sequence of the triacylglycerols matches the published data for linseed as a source to extract triacylglycerol compounds.
• Can Nonrandomized Experiments Yield Accurate Answers?
• What Kind of Experiments Are Done on Animals?
• Is It Good to Use Animals for Experiments?
• What Are the Types of Experiments?
• Is There Any Healthy Way to Experiment With Drugs?
• What Are the Top Experiments of All Time?
• Are Breaching Experiments Ethical?
• What Does It Mean to Experiment With a Drug?
• Why Do We Use Factorial Experiments?
• How Does Temperature Affect the Rate of Reaction Experiment?
• What Are the Easiest Experiments to Do?
• How Can Rushing Harm the Data and the Experiment Overall?
• What Are the Steps to a Science Experiment?
• How Do Errors Affect the Experiment?
• What Is the Purpose of the Wax Experiment and What Conclusion Does Descartes Reach on Its Basis?
• Can an Experiment Be Invalid but Reliable?
• What Is the Most Influential Experiment in Psychology?
• Why Are Fruit Flies Used for Experiments?
• How Can You Improve the Accuracy of an Experiment?
• What Was Galileo’s Famous Cannonball Drop Experiment?
• What Can Knowledge Be Gained From Conducting a Breaching Experiment?
• How Do You Identify the Independent and Dependent Variables in an Experiment?
• What Was Griffith’s Experiment and Why Was It Important?
• What Is the Difference Between Contingent Valuation and Choice Experiment?
• What Is the Choice Experiment Valuation Method?
• An Enzyme Linked Immunosorbent Assay Experiment In our society presently, immunoassay techniques used in data analyses have assumed a place of high significance, particularly as it applies to pure/applied research.
• Anaerobic Threshold: An Experiment Anaerobic Threshold refers to the minimum level below which no increase in blood lactose can occur. At levels above AT, supplementing aerobic production needs aerobic energy.
• Comparative Effectiveness of Various Surfactants: Experiment Surfactants refer to chemical substances that lessen the surface tension in water. This experiment aimed at establishing the comparative effectiveness of various surfactants.
• A Hypothesis and an Experiment: A Case Study On the control experiment, there would be a seed grown at normal aeration, and wind conditions. All should have a viable bean seed planted centrally on watered soil preferably.
• Bolted & Welded Connections and Tension Experiment Exploring and comparing the expected and actual failure modes of both bottled and welded connections in tension are the primary purposes of the paper.
• Lab Experiment on Photovoltaics The experiment was done specifically to ascertain how various connected units could be coordinated to give a more reliable and controllable functioning.
• Mind Control: Ethics of the Experiment The topics of mind control and free will has always been seen as a morally grey area in terms of its research potential.
• Jane Elliott’s Experiment on Discrimination The teacher Jane Elliott from Iowa decided to conduct an experiment demonstrating to her students what discrimination is and what it feels like.
• Ideal Experiment Design: Independent and Dependent Variables This work describes the ideal experiment, that is designed to verify the causal relationship between independent and dependent variables.
• The Tuskegee Syphilis Experiment When the Tuskegee Syphilis Experiment was begun, over 75 years ago, no such principles were officially in place.
• The Power of Conformity: Asch’s Experiments The article examines a series of experiments by Asch that helped him identify the factors influencing social conformity.
• The Critical Characteristics of an Experiment The main aim of this assignment is to evaluate the thought control experiment by famous psychologist Ellen Langer and determine whether it is a qualitative experiment.
• Milgram Experiment: The Question of Ethics This essay will discuss the Milgram experiment and also argue that it was ethical as medical research standards were met, and no undue harm to the participants was caused.
• Boston’s Experiment: Harvard Business Review’s Lessons In Harvard Business Review’s Lessons from Boston’s Experiment with The One Fund, Mitchell discusses his experience with fund distribution to the victims of the Boston bombing.
• The Way to Come To Terms With Yourself: Social Distancing Experiment In this work, the author describes the course and results of an experiment on social distance: refusal to use gadgets, any communication, and going out.
• Experiment: Bacteria vs Antibiotics The experiment aimed was to test the reaction of bacteria towards some antibiotics and determine the effectiveness of those antibiotics in treating some diseases.
• Chemical Experiment on Enzyme Amylase This paper presents an experiment that was conducted to determine the activity of amylase on starch at various pH levels.
• Ethics: Experiments on Animals Industrial and biomedical research is often painful and most of the test ends up killing the animals. Experiments such as these often incur the wrath of the animal rights movement.
• Impact of the Stanford Prison Experiment Have on Psychology This essay will begin with a brief description of Zimbardo’s Stanford Prison Experiment then it will move to explore two main issues that arose from the said experiment.
• Medical Pharmacology: The Langendorff Experiment The Langendorff experiment aimed at using an ex vivo isolated rat heart preparation to demonstrate the pharmacological effects of two unknown drugs.
• Studying Organisations: The Hawthorne Experiments The Hawthorn experiments marked a new direction in research of motivation and productivity. More than half a century has passed, and productivity remains a concern of management.
• Chemistry of Cooking. Saffron Rice Experiment This research project outlines an experiment that aims to determine the temperature at which Saffron rice turns yellow.
• Evaluation of the Stanford Prison Experiment’ Role The Stanford Prison Experiment is a study that was conducted on August 20, 1971 by a group of researchers headed by the psychology professor Philip Zimbardo.
• Social Experiment: Informal Norms of Gender Issues The social experiment presents a contradiction between the socially-accepted norms and the understanding of equality between men and women.
• Social Experiment: Wrong Outfit in a Wedding Event The attendees of the wedding event displayed disappointment, discomfort, and open resentment towards the dressing style.
• Heat Transfer Rates in a Hot Jet: Experiment The experiment is aimed at determining the heat transfer rates in a hot jet. The reasons for the hot jet to have different heat rates in different areas will be determined.
• Inattentive Blindness in Psychological Experiment The features of the human consciousness not to notice quite obvious changes are natural and innate. Such blindness can be caused by several factors.
• Situation, Institutional Norms, and Roles: The Stanford Experiment of Zimbardo Philip Zimbardo’s Stanford Experiment brought him critical acclaim. At the same time, it accorded him a certain level of notoriety; because of the methodologies he utilized to conduct the experiment.
• Pasture Experiment: Fertiliser Treatments Response This work is an experiment that defines the role of fertilizers in pasture production and to establish the appropriate use of pasture sampling to assess pasture mass.
• Tuskegee Syphilis Experiment: Ethical Controversy Tuskegee case set the background for the reconsideration of healthcare ethics, which means that the ethical value of the given case deserves reconsideration.
• Gender Stereotyping Experiment: The Level of Gender Stereotyping in Society The present study measures the effects of stereotyping women. It examines the first impression formed by subjects based on the information about a fictitious man or a woman.
• Psychological Studies and Experiments: Code of Conduct The following paper is based on past psychological studies i.e. Stanly Milgram’s ‘Obedience Experiment’, Philip Zimbardo’s ‘Stanford Prison Experiment, and Jane Elliott’s ‘Class Divided’.
• Scientific Experiments on Animals from Ethical Perspectives This paper discusses using animals in scientific experiments from the consequentialist, Kantian deontological and Donna Yarri’s Christian character-based perspectives.
• Using Animals in Medical Experiments This paper explores how the principles of the character-based ethical approach can be applied to the discussion of using animals in the medical research and experiments.
• The Stanford Experiment by Philip Zimbardo Philip Zimbardo’s Stanford Experiment shows that situational power and norms dictate the behavior of the individual more than the core beliefs that made up his personal identity.

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These essay examples and topics on Experiment were carefully selected by the StudyCorgi editorial team. They meet our highest standards in terms of grammar, punctuation, style, and fact accuracy. Please ensure you properly reference the materials if you’re using them to write your assignment.

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Experimental Research

Experimental research is commonly used in sciences such as sociology and psychology, physics, chemistry, biology and medicine etc.

This article is a part of the guide:

• Pretest-Posttest
• Third Variable
• Research Bias
• Independent Variable
• Between Subjects

Browse Full Outline

• 1 Experimental Research
• 2.1 Independent Variable
• 2.2 Dependent Variable
• 2.3 Controlled Variables
• 2.4 Third Variable
• 3.1 Control Group
• 3.2 Research Bias
• 3.3.1 Placebo Effect
• 3.3.2 Double Blind Method
• 4.1 Randomized Controlled Trials
• 4.2 Pretest-Posttest
• 4.3 Solomon Four Group
• 4.4 Between Subjects
• 4.5 Within Subject
• 4.6 Repeated Measures
• 4.7 Counterbalanced Measures
• 4.8 Matched Subjects

It is a collection of research designs which use manipulation and controlled testing to understand causal processes. Generally, one or more variables are manipulated to determine their effect on a dependent variable.

The experimental method is a systematic and scientific approach to research in which the researcher manipulates one or more variables, and controls and measures any change in other variables.

Experimental Research is often used where:

• There is time priority in a causal relationship ( cause precedes effect )
• There is consistency in a causal relationship (a cause will always lead to the same effect)
• The magnitude of the correlation is great.

(Reference: en.wikipedia.org)

The word experimental research has a range of definitions. In the strict sense, experimental research is what we call a true experiment .

This is an experiment where the researcher manipulates one variable, and control/randomizes the rest of the variables. It has a control group , the subjects have been randomly assigned between the groups, and the researcher only tests one effect at a time. It is also important to know what variable(s) you want to test and measure.

A very wide definition of experimental research, or a quasi experiment , is research where the scientist actively influences something to observe the consequences. Most experiments tend to fall in between the strict and the wide definition.

A rule of thumb is that physical sciences, such as physics, chemistry and geology tend to define experiments more narrowly than social sciences, such as sociology and psychology, which conduct experiments closer to the wider definition.

Aims of Experimental Research

Experiments are conducted to be able to predict phenomenons. Typically, an experiment is constructed to be able to explain some kind of causation . Experimental research is important to society - it helps us to improve our everyday lives.

Identifying the Research Problem

After deciding the topic of interest, the researcher tries to define the research problem . This helps the researcher to focus on a more narrow research area to be able to study it appropriately.  Defining the research problem helps you to formulate a  research hypothesis , which is tested against the  null hypothesis .

The research problem is often operationalizationed , to define how to measure the research problem. The results will depend on the exact measurements that the researcher chooses and may be operationalized differently in another study to test the main conclusions of the study.

An ad hoc analysis is a hypothesis invented after testing is done, to try to explain why the contrary evidence. A poor ad hoc analysis may be seen as the researcher's inability to accept that his/her hypothesis is wrong, while a great ad hoc analysis may lead to more testing and possibly a significant discovery.

Constructing the Experiment

There are various aspects to remember when constructing an experiment. Planning ahead ensures that the experiment is carried out properly and that the results reflect the real world, in the best possible way.

Sampling Groups to Study

Sampling groups correctly is especially important when we have more than one condition in the experiment. One sample group often serves as a control group , whilst others are tested under the experimental conditions.

Deciding the sample groups can be done in using many different sampling techniques. Population sampling may chosen by a number of methods, such as randomization , "quasi-randomization" and pairing.

Reducing sampling errors is vital for getting valid results from experiments. Researchers often adjust the sample size to minimize chances of random errors .

Here are some common sampling techniques :

• probability sampling
• non-probability sampling
• simple random sampling
• convenience sampling
• stratified sampling
• systematic sampling
• cluster sampling
• sequential sampling
• disproportional sampling
• judgmental sampling
• snowball sampling
• quota sampling

Creating the Design

The research design is chosen based on a range of factors. Important factors when choosing the design are feasibility, time, cost, ethics, measurement problems and what you would like to test. The design of the experiment is critical for the validity of the results.

Typical Designs and Features in Experimental Design

• Pretest-Posttest Design Check whether the groups are different before the manipulation starts and the effect of the manipulation. Pretests sometimes influence the effect.
• Control Group Control groups are designed to measure research bias and measurement effects, such as the Hawthorne Effect or the Placebo Effect . A control group is a group not receiving the same manipulation as the experimental group. Experiments frequently have 2 conditions, but rarely more than 3 conditions at the same time.
• Randomized Controlled Trials Randomized Sampling, comparison between an Experimental Group and a Control Group and strict control/randomization of all other variables
• Solomon Four-Group Design With two control groups and two experimental groups. Half the groups have a pretest and half do not have a pretest. This to test both the effect itself and the effect of the pretest.
• Between Subjects Design Grouping Participants to Different Conditions
• Within Subject Design Participants Take Part in the Different Conditions - See also: Repeated Measures Design
• Counterbalanced Measures Design Testing the effect of the order of treatments when no control group is available/ethical
• Matched Subjects Design Matching Participants to Create Similar Experimental- and Control-Groups
• Double-Blind Experiment Neither the researcher, nor the participants, know which is the control group. The results can be affected if the researcher or participants know this.
• Bayesian Probability Using bayesian probability to "interact" with participants is a more "advanced" experimental design. It can be used for settings were there are many variables which are hard to isolate. The researcher starts with a set of initial beliefs, and tries to adjust them to how participants have responded

Pilot Study

It may be wise to first conduct a pilot-study or two before you do the real experiment. This ensures that the experiment measures what it should, and that everything is set up right.

Minor errors, which could potentially destroy the experiment, are often found during this process. With a pilot study, you can get information about errors and problems, and improve the design, before putting a lot of effort into the real experiment.

If the experiments involve humans, a common strategy is to first have a pilot study with someone involved in the research, but not too closely, and then arrange a pilot with a person who resembles the subject(s) . Those two different pilots are likely to give the researcher good information about any problems in the experiment.

Conducting the Experiment

An experiment is typically carried out by manipulating a variable, called the independent variable , affecting the experimental group. The effect that the researcher is interested in, the dependent variable(s) , is measured.

Identifying and controlling non-experimental factors which the researcher does not want to influence the effects, is crucial to drawing a valid conclusion. This is often done by controlling variables , if possible, or randomizing variables to minimize effects that can be traced back to third variables . Researchers only want to measure the effect of the independent variable(s) when conducting an experiment , allowing them to conclude that this was the reason for the effect.

Analysis and Conclusions

In quantitative research , the amount of data measured can be enormous. Data not prepared to be analyzed is called "raw data". The raw data is often summarized as something called "output data", which typically consists of one line per subject (or item). A cell of the output data is, for example, an average of an effect in many trials for a subject. The output data is used for statistical analysis, e.g. significance tests, to see if there really is an effect.

The aim of an analysis is to draw a conclusion , together with other observations. The researcher might generalize the results to a wider phenomenon, if there is no indication of confounding variables "polluting" the results.

If the researcher suspects that the effect stems from a different variable than the independent variable, further investigation is needed to gauge the validity of the results. An experiment is often conducted because the scientist wants to know if the independent variable is having any effect upon the dependent variable. Variables correlating are not proof that there is causation .

Experiments are more often of quantitative nature than qualitative nature, although it happens.

Examples of Experiments

This website contains many examples of experiments. Some are not true experiments , but involve some kind of manipulation to investigate a phenomenon. Others fulfill most or all criteria of true experiments.

Here are some examples of scientific experiments:

Social Psychology

• Stanley Milgram Experiment - Will people obey orders, even if clearly dangerous?
• Asch Experiment - Will people conform to group behavior?
• Stanford Prison Experiment - How do people react to roles? Will you behave differently?
• Good Samaritan Experiment - Would You Help a Stranger? - Explaining Helping Behavior
• Law Of Segregation - The Mendel Pea Plant Experiment
• Transforming Principle - Griffith's Experiment about Genetics
• Ben Franklin Kite Experiment - Struck by Lightning
• J J Thomson Cathode Ray Experiment
• Psychology 101
• Flags and Countries
• Capitals and Countries

Oskar Blakstad (Jul 10, 2008). Experimental Research. Retrieved Sep 07, 2024 from Explorable.com: https://explorable.com/experimental-research

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• Experimental Research Designs: Types, Examples & Methods

Experimental research is the most familiar type of research design for individuals in the physical sciences and a host of other fields. This is mainly because experimental research is a classical scientific experiment, similar to those performed in high school science classes.

Imagine taking 2 samples of the same plant and exposing one of them to sunlight, while the other is kept away from sunlight. Let the plant exposed to sunlight be called sample A, while the latter is called sample B.

If after the duration of the research, we find out that sample A grows and sample B dies, even though they are both regularly wetted and given the same treatment. Therefore, we can conclude that sunlight will aid growth in all similar plants.

What is Experimental Research?

Experimental research is a scientific approach to research, where one or more independent variables are manipulated and applied to one or more dependent variables to measure their effect on the latter. The effect of the independent variables on the dependent variables is usually observed and recorded over some time, to aid researchers in drawing a reasonable conclusion regarding the relationship between these 2 variable types.

The experimental research method is widely used in physical and social sciences, psychology, and education. It is based on the comparison between two or more groups with a straightforward logic, which may, however, be difficult to execute.

Mostly related to a laboratory test procedure, experimental research designs involve collecting quantitative data and performing statistical analysis on them during research. Therefore, making it an example of quantitative research method .

What are The Types of Experimental Research Design?

The types of experimental research design are determined by the way the researcher assigns subjects to different conditions and groups. They are of 3 types, namely; pre-experimental, quasi-experimental, and true experimental research.

Pre-experimental Research Design

In pre-experimental research design, either a group or various dependent groups are observed for the effect of the application of an independent variable which is presumed to cause change. It is the simplest form of experimental research design and is treated with no control group.

Although very practical, experimental research is lacking in several areas of the true-experimental criteria. The pre-experimental research design is further divided into three types

• One-shot Case Study Research Design

In this type of experimental study, only one dependent group or variable is considered. The study is carried out after some treatment which was presumed to cause change, making it a posttest study.

• One-group Pretest-posttest Research Design: 

This research design combines both posttest and pretest study by carrying out a test on a single group before the treatment is administered and after the treatment is administered. With the former being administered at the beginning of treatment and later at the end.

• Static-group Comparison: 

In a static-group comparison study, 2 or more groups are placed under observation, where only one of the groups is subjected to some treatment while the other groups are held static. All the groups are post-tested, and the observed differences between the groups are assumed to be a result of the treatment.

Quasi-experimental Research Design

  The word “quasi” means partial, half, or pseudo. Therefore, the quasi-experimental research bearing a resemblance to the true experimental research, but not the same.  In quasi-experiments, the participants are not randomly assigned, and as such, they are used in settings where randomization is difficult or impossible.

 This is very common in educational research, where administrators are unwilling to allow the random selection of students for experimental samples.

Some examples of quasi-experimental research design include; the time series, no equivalent control group design, and the counterbalanced design.

True Experimental Research Design

The true experimental research design relies on statistical analysis to approve or disprove a hypothesis. It is the most accurate type of experimental design and may be carried out with or without a pretest on at least 2 randomly assigned dependent subjects.

The true experimental research design must contain a control group, a variable that can be manipulated by the researcher, and the distribution must be random. The classification of true experimental design include:

• The posttest-only Control Group Design: In this design, subjects are randomly selected and assigned to the 2 groups (control and experimental), and only the experimental group is treated. After close observation, both groups are post-tested, and a conclusion is drawn from the difference between these groups.
• The pretest-posttest Control Group Design: For this control group design, subjects are randomly assigned to the 2 groups, both are presented, but only the experimental group is treated. After close observation, both groups are post-tested to measure the degree of change in each group.
• Solomon four-group Design: This is the combination of the pretest-only and the pretest-posttest control groups. In this case, the randomly selected subjects are placed into 4 groups.

The first two of these groups are tested using the posttest-only method, while the other two are tested using the pretest-posttest method.

Examples of Experimental Research

Experimental research examples are different, depending on the type of experimental research design that is being considered. The most basic example of experimental research is laboratory experiments, which may differ in nature depending on the subject of research.

Administering Exams After The End of Semester

During the semester, students in a class are lectured on particular courses and an exam is administered at the end of the semester. In this case, the students are the subjects or dependent variables while the lectures are the independent variables treated on the subjects.

Only one group of carefully selected subjects are considered in this research, making it a pre-experimental research design example. We will also notice that tests are only carried out at the end of the semester, and not at the beginning.

Further making it easy for us to conclude that it is a one-shot case study research. 

Employee Skill Evaluation

Before employing a job seeker, organizations conduct tests that are used to screen out less qualified candidates from the pool of qualified applicants. This way, organizations can determine an employee’s skill set at the point of employment.

In the course of employment, organizations also carry out employee training to improve employee productivity and generally grow the organization. Further evaluation is carried out at the end of each training to test the impact of the training on employee skills, and test for improvement.

Here, the subject is the employee, while the treatment is the training conducted. This is a pretest-posttest control group experimental research example.

Evaluation of Teaching Method

Let us consider an academic institution that wants to evaluate the teaching method of 2 teachers to determine which is best. Imagine a case whereby the students assigned to each teacher is carefully selected probably due to personal request by parents or due to stubbornness and smartness.

This is a no equivalent group design example because the samples are not equal. By evaluating the effectiveness of each teacher’s teaching method this way, we may conclude after a post-test has been carried out.

However, this may be influenced by factors like the natural sweetness of a student. For example, a very smart student will grab more easily than his or her peers irrespective of the method of teaching.

What are the Characteristics of Experimental Research?  

Experimental research contains dependent, independent and extraneous variables. The dependent variables are the variables being treated or manipulated and are sometimes called the subject of the research.

The independent variables are the experimental treatment being exerted on the dependent variables. Extraneous variables, on the other hand, are other factors affecting the experiment that may also contribute to the change.

The setting is where the experiment is carried out. Many experiments are carried out in the laboratory, where control can be exerted on the extraneous variables, thereby eliminating them.

Other experiments are carried out in a less controllable setting. The choice of setting used in research depends on the nature of the experiment being carried out.

• Multivariable

Experimental research may include multiple independent variables, e.g. time, skills, test scores, etc.

Why Use Experimental Research Design?  

Experimental research design can be majorly used in physical sciences, social sciences, education, and psychology. It is used to make predictions and draw conclusions on a subject matter. 

Some uses of experimental research design are highlighted below.

• Medicine: Experimental research is used to provide the proper treatment for diseases. In most cases, rather than directly using patients as the research subject, researchers take a sample of the bacteria from the patient’s body and are treated with the developed antibacterial

The changes observed during this period are recorded and evaluated to determine its effectiveness. This process can be carried out using different experimental research methods.

• Education: Asides from science subjects like Chemistry and Physics which involves teaching students how to perform experimental research, it can also be used in improving the standard of an academic institution. This includes testing students’ knowledge on different topics, coming up with better teaching methods, and the implementation of other programs that will aid student learning.
• Human Behavior: Social scientists are the ones who mostly use experimental research to test human behaviour. For example, consider 2 people randomly chosen to be the subject of the social interaction research where one person is placed in a room without human interaction for 1 year.

The other person is placed in a room with a few other people, enjoying human interaction. There will be a difference in their behaviour at the end of the experiment.

• UI/UX: During the product development phase, one of the major aims of the product team is to create a great user experience with the product. Therefore, before launching the final product design, potential are brought in to interact with the product.

For example, when finding it difficult to choose how to position a button or feature on the app interface, a random sample of product testers are allowed to test the 2 samples and how the button positioning influences the user interaction is recorded.

What are the Disadvantages of Experimental Research?  

• It is highly prone to human error due to its dependency on variable control which may not be properly implemented. These errors could eliminate the validity of the experiment and the research being conducted.
• Exerting control of extraneous variables may create unrealistic situations. Eliminating real-life variables will result in inaccurate conclusions. This may also result in researchers controlling the variables to suit his or her personal preferences.
• It is a time-consuming process. So much time is spent on testing dependent variables and waiting for the effect of the manipulation of dependent variables to manifest.
• It is expensive.
• It is very risky and may have ethical complications that cannot be ignored. This is common in medical research, where failed trials may lead to a patient’s death or a deteriorating health condition.
• Experimental research results are not descriptive.
• Response bias can also be supplied by the subject of the conversation.
• Human responses in experimental research can be difficult to measure.

What are the Data Collection Methods in Experimental Research?  

Data collection methods in experimental research are the different ways in which data can be collected for experimental research. They are used in different cases, depending on the type of research being carried out.

1. Observational Study

This type of study is carried out over a long period. It measures and observes the variables of interest without changing existing conditions.

When researching the effect of social interaction on human behavior, the subjects who are placed in 2 different environments are observed throughout the research. No matter the kind of absurd behavior that is exhibited by the subject during this period, its condition will not be changed.

This may be a very risky thing to do in medical cases because it may lead to death or worse medical conditions.

2. Simulations

This procedure uses mathematical, physical, or computer models to replicate a real-life process or situation. It is frequently used when the actual situation is too expensive, dangerous, or impractical to replicate in real life.

This method is commonly used in engineering and operational research for learning purposes and sometimes as a tool to estimate possible outcomes of real research. Some common situation software are Simulink, MATLAB, and Simul8.

Not all kinds of experimental research can be carried out using simulation as a data collection tool . It is very impractical for a lot of laboratory-based research that involves chemical processes.

A survey is a tool used to gather relevant data about the characteristics of a population and is one of the most common data collection tools. A survey consists of a group of questions prepared by the researcher, to be answered by the research subject.

Surveys can be shared with the respondents both physically and electronically. When collecting data through surveys, the kind of data collected depends on the respondent, and researchers have limited control over it.

Formplus is the best tool for collecting experimental data using survey s. It has relevant features that will aid the data collection process and can also be used in other aspects of experimental research.

Differences between Experimental and Non-Experimental Research 

1. In experimental research, the researcher can control and manipulate the environment of the research, including the predictor variable which can be changed. On the other hand, non-experimental research cannot be controlled or manipulated by the researcher at will.

This is because it takes place in a real-life setting, where extraneous variables cannot be eliminated. Therefore, it is more difficult to conclude non-experimental studies, even though they are much more flexible and allow for a greater range of study fields.

2. The relationship between cause and effect cannot be established in non-experimental research, while it can be established in experimental research. This may be because many extraneous variables also influence the changes in the research subject, making it difficult to point at a particular variable as the cause of a particular change

3. Independent variables are not introduced, withdrawn, or manipulated in non-experimental designs, but the same may not be said about experimental research.

Experimental Research vs. Alternatives and When to Use Them

1. experimental research vs causal comparative.

Experimental research enables you to control variables and identify how the independent variable affects the dependent variable. Causal-comparative find out the cause-and-effect relationship between the variables by comparing already existing groups that are affected differently by the independent variable.

For example, in an experiment to see how K-12 education affects children and teenager development. An experimental research would split the children into groups, some would get formal K-12 education, while others won’t. This is not ethically right because every child has the right to education. So, what we do instead would be to compare already existing groups of children who are getting formal education with those who due to some circumstances can not.

Pros and Cons of Experimental vs Causal-Comparative Research

• Causal-Comparative:   Strengths:  More realistic than experiments, can be conducted in real-world settings.  Weaknesses:  Establishing causality can be weaker due to the lack of manipulation.

2. Experimental Research vs Correlational Research

When experimenting, you are trying to establish a cause-and-effect relationship between different variables. For example, you are trying to establish the effect of heat on water, the temperature keeps changing (independent variable) and you see how it affects the water (dependent variable).

For correlational research, you are not necessarily interested in the why or the cause-and-effect relationship between the variables, you are focusing on the relationship. Using the same water and temperature example, you are only interested in the fact that they change, you are not investigating which of the variables or other variables causes them to change.

Pros and Cons of Experimental vs Correlational Research

3. experimental research vs descriptive research.

With experimental research, you alter the independent variable to see how it affects the dependent variable, but with descriptive research you are simply studying the characteristics of the variable you are studying.

So, in an experiment to see how blown glass reacts to temperature, experimental research would keep altering the temperature to varying levels of high and low to see how it affects the dependent variable (glass). But descriptive research would investigate the glass properties.

Pros and Cons of Experimental vs Descriptive Research

4. experimental research vs action research.

Experimental research tests for causal relationships by focusing on one independent variable vs the dependent variable and keeps other variables constant. So, you are testing hypotheses and using the information from the research to contribute to knowledge.

However, with action research, you are using a real-world setting which means you are not controlling variables. You are also performing the research to solve actual problems and improve already established practices.

For example, if you are testing for how long commutes affect workers’ productivity. With experimental research, you would vary the length of commute to see how the time affects work. But with action research, you would account for other factors such as weather, commute route, nutrition, etc. Also, experimental research helps know the relationship between commute time and productivity, while action research helps you look for ways to improve productivity

Pros and Cons of Experimental vs Action Research

Conclusion  .

Experimental research designs are often considered to be the standard in research designs. This is partly due to the common misconception that research is equivalent to scientific experiments—a component of experimental research design.

In this research design, one or more subjects or dependent variables are randomly assigned to different treatments (i.e. independent variables manipulated by the researcher) and the results are observed to conclude. One of the uniqueness of experimental research is in its ability to control the effect of extraneous variables.

Experimental research is suitable for research whose goal is to examine cause-effect relationships, e.g. explanatory research. It can be conducted in the laboratory or field settings, depending on the aim of the research that is being carried out. 

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16 Advantages and Disadvantages of Experimental Research

How do you make sure that a new product, theory, or idea has validity? There are multiple ways to test them, with one of the most common being the use of experimental research. When there is complete control over one variable, the other variables can be manipulated to determine the value or validity that has been proposed.

Then, through a process of monitoring and administration, the true effects of what is being studied can be determined. This creates an accurate outcome so conclusions about the final value potential. It is an efficient process, but one that can also be easily manipulated to meet specific metrics if oversight is not properly performed.

Here are the advantages and disadvantages of experimental research to consider.

What Are the Advantages of Experimental Research?

1. It provides researchers with a high level of control. By being able to isolate specific variables, it becomes possible to determine if a potential outcome is viable. Each variable can be controlled on its own or in different combinations to study what possible outcomes are available for a product, theory, or idea as well. This provides a tremendous advantage in an ability to find accurate results.

2. There is no limit to the subject matter or industry involved. Experimental research is not limited to a specific industry or type of idea. It can be used in a wide variety of situations. Teachers might use experimental research to determine if a new method of teaching or a new curriculum is better than an older system. Pharmaceutical companies use experimental research to determine the viability of a new product.

3. Experimental research provides conclusions that are specific. Because experimental research provides such a high level of control, it can produce results that are specific and relevant with consistency. It is possible to determine success or failure, making it possible to understand the validity of a product, theory, or idea in a much shorter amount of time compared to other verification methods. You know the outcome of the research because you bring the variable to its conclusion.

4. The results of experimental research can be duplicated. Experimental research is straightforward, basic form of research that allows for its duplication when the same variables are controlled by others. This helps to promote the validity of a concept for products, ideas, and theories. This allows anyone to be able to check and verify published results, which often allows for better results to be achieved, because the exact steps can produce the exact results.

5. Natural settings can be replicated with faster speeds. When conducting research within a laboratory environment, it becomes possible to replicate conditions that could take a long time so that the variables can be tested appropriately. This allows researchers to have a greater control of the extraneous variables which may exist as well, limiting the unpredictability of nature as each variable is being carefully studied.

6. Experimental research allows cause and effect to be determined. The manipulation of variables allows for researchers to be able to look at various cause-and-effect relationships that a product, theory, or idea can produce. It is a process which allows researchers to dig deeper into what is possible, showing how the various variable relationships can provide specific benefits. In return, a greater understanding of the specifics within the research can be understood, even if an understanding of why that relationship is present isn’t presented to the researcher.

7. It can be combined with other research methods. This allows experimental research to be able to provide the scientific rigor that may be needed for the results to stand on their own. It provides the possibility of determining what may be best for a specific demographic or population while also offering a better transference than anecdotal research can typically provide.

What Are the Disadvantages of Experimental Research?

1. Results are highly subjective due to the possibility of human error. Because experimental research requires specific levels of variable control, it is at a high risk of experiencing human error at some point during the research. Any error, whether it is systemic or random, can reveal information about the other variables and that would eliminate the validity of the experiment and research being conducted.

2. Experimental research can create situations that are not realistic. The variables of a product, theory, or idea are under such tight controls that the data being produced can be corrupted or inaccurate, but still seem like it is authentic. This can work in two negative ways for the researcher. First, the variables can be controlled in such a way that it skews the data toward a favorable or desired result. Secondly, the data can be corrupted to seem like it is positive, but because the real-life environment is so different from the controlled environment, the positive results could never be achieved outside of the experimental research.

3. It is a time-consuming process. For it to be done properly, experimental research must isolate each variable and conduct testing on it. Then combinations of variables must also be considered. This process can be lengthy and require a large amount of financial and personnel resources. Those costs may never be offset by consumer sales if the product or idea never makes it to market. If what is being tested is a theory, it can lead to a false sense of validity that may change how others approach their own research.

4. There may be ethical or practical problems with variable control. It might seem like a good idea to test new pharmaceuticals on animals before humans to see if they will work, but what happens if the animal dies because of the experimental research? Or what about human trials that fail and cause injury or death? Experimental research might be effective, but sometimes the approach has ethical or practical complications that cannot be ignored. Sometimes there are variables that cannot be manipulated as it should be so that results can be obtained.

5. Experimental research does not provide an actual explanation. Experimental research is an opportunity to answer a Yes or No question. It will either show you that it will work or it will not work as intended. One could argue that partial results could be achieved, but that would still fit into the “No” category because the desired results were not fully achieved. The answer is nice to have, but there is no explanation as to how you got to that answer. Experimental research is unable to answer the question of “Why” when looking at outcomes.

6. Extraneous variables cannot always be controlled. Although laboratory settings can control extraneous variables, natural environments provide certain challenges. Some studies need to be completed in a natural setting to be accurate. It may not always be possible to control the extraneous variables because of the unpredictability of Mother Nature. Even if the variables are controlled, the outcome may ensure internal validity, but do so at the expense of external validity. Either way, applying the results to the general population can be quite challenging in either scenario.

7. Participants can be influenced by their current situation. Human error isn’t just confined to the researchers. Participants in an experimental research study can also be influenced by extraneous variables. There could be something in the environment, such an allergy, that creates a distraction. In a conversation with a researcher, there may be a physical attraction that changes the responses of the participant. Even internal triggers, such as a fear of enclosed spaces, could influence the results that are obtained. It is also very common for participants to “go along” with what they think a researcher wants to see instead of providing an honest response.

8. Manipulating variables isn’t necessarily an objective standpoint. For research to be effective, it must be objective. Being able to manipulate variables reduces that objectivity. Although there are benefits to observing the consequences of such manipulation, those benefits may not provide realistic results that can be used in the future. Taking a sample is reflective of that sample and the results may not translate over to the general population.

9. Human responses in experimental research can be difficult to measure. There are many pressures that can be placed on people, from political to personal, and everything in-between. Different life experiences can cause people to react to the same situation in different ways. Not only does this mean that groups may not be comparable in experimental research, but it also makes it difficult to measure the human responses that are obtained or observed.

The advantages and disadvantages of experimental research show that it is a useful system to use, but it must be tightly controlled in order to be beneficial. It produces results that can be replicated, but it can also be easily influenced by internal or external influences that may alter the outcomes being achieved. By taking these key points into account, it will become possible to see if this research process is appropriate for your next product, theory, or idea.

Experimental Research: Meaning And Examples Of Experimental Research

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every…

Ever wondered why scientists across the world are being lauded for discovering the Covid-19 vaccine so early? It’s because every government knows that vaccines are a result of experimental research design and it takes years of collected data to make one. It takes a lot of time to compare formulas and combinations with an array of possibilities across different age groups, genders and physical conditions. With their efficiency and meticulousness, scientists redefined the meaning of experimental research when they discovered a vaccine in less than a year.

What Is Experimental Research?

Characteristics of experimental research design, types of experimental research design, advantages and disadvantages of experimental research, examples of experimental research.

Experimental research is a scientific method of conducting research using two variables: independent and dependent. Independent variables can be manipulated to apply to dependent variables and the effect is measured. This measurement usually happens over a significant period of time to establish conditions and conclusions about the relationship between these two variables.

Experimental research is widely implemented in education, psychology, social sciences and physical sciences. Experimental research is based on observation, calculation, comparison and logic. Researchers collect quantitative data and perform statistical analyses of two sets of variables. This method collects necessary data to focus on facts and support sound decisions. It’s a helpful approach when time is a factor in establishing cause-and-effect relationships or when an invariable behavior is seen between the two.  

Now that we know the meaning of experimental research, let’s look at its characteristics, types and advantages.

The hypothesis is at the core of an experimental research design. Researchers propose a tentative answer after defining the problem and then test the hypothesis to either confirm or disregard it. Here are a few characteristics of experimental research:

• Dependent variables are manipulated or treated while independent variables are exerted on dependent variables as an experimental treatment. Extraneous variables are variables generated from other factors that can affect the experiment and contribute to change. Researchers have to exercise control to reduce the influence of these variables by randomization, making homogeneous groups and applying statistical analysis techniques.
• Researchers deliberately operate independent variables on the subject of the experiment. This is known as manipulation.
• Once a variable is manipulated, researchers observe the effect an independent variable has on a dependent variable. This is key for interpreting results.
• A researcher may want multiple comparisons between different groups with equivalent subjects. They may replicate the process by conducting sub-experiments within the framework of the experimental design.

Experimental research is equally effective in non-laboratory settings as it is in labs. It helps in predicting events in an experimental setting. It generalizes variable relationships so that they can be implemented outside the experiment and applied to a wider interest group.

The way a researcher assigns subjects to different groups determines the types of experimental research design .

Pre-experimental Research Design

In a pre-experimental research design, researchers observe a group or various groups to see the effect an independent variable has on the dependent variable to cause change. There is no control group as it is a simple form of experimental research . It’s further divided into three categories:

• A one-shot case study research design is a study where one dependent variable is considered. It’s a posttest study as it’s carried out after treating what presumably caused the change.
• One-group pretest-posttest design is a study that combines both pretest and posttest studies by testing a single group before and after administering the treatment.
• Static-group comparison involves studying two groups by subjecting one to treatment while the other remains static. After post-testing all groups the differences are observed.

This design is practical but lacks in certain areas of true experimental criteria.

True Experimental Research Design

This design depends on statistical analysis to approve or disregard a hypothesis. It’s an accurate design that can be conducted with or without a pretest on a minimum of two dependent variables assigned randomly. It is further classified into three types:

• The posttest-only control group design involves randomly selecting and assigning subjects to two groups: experimental and control. Only the experimental group is treated, while both groups are observed and post-tested to draw a conclusion from the difference between the groups.
• In a pretest-posttest control group design, two groups are randomly assigned subjects. Both groups are presented, the experimental group is treated and both groups are post-tested to measure how much change happened in each group.
• Solomon four-group design is a combination of the previous two methods. Subjects are randomly selected and assigned to four groups. Two groups are tested using each of the previous methods.

True experimental research design should have a variable to manipulate, a control group and random distribution.

With experimental research, we can test ideas in a controlled environment before marketing. It acts as the best method to test a theory as it can help in making predictions about a subject and drawing conclusions. Let’s look at some of the advantages that make experimental research useful:

• It allows researchers to have a stronghold over variables and collect desired results.
• Results are usually specific.
• The effectiveness of the research isn’t affected by the subject.
• Findings from the results usually apply to similar situations and ideas.
• Cause and effect of a hypothesis can be identified, which can be further analyzed for in-depth ideas.
• It’s the ideal starting point to collect data and lay a foundation for conducting further research and building more ideas.
• Medical researchers can develop medicines and vaccines to treat diseases by collecting samples from patients and testing them under multiple conditions.
• It can be used to improve the standard of academics across institutions by testing student knowledge and teaching methods before analyzing the result to implement programs.
• Social scientists often use experimental research design to study and test behavior in humans and animals.
• Software development and testing heavily depend on experimental research to test programs by letting subjects use a beta version and analyzing their feedback.

Even though it’s a scientific method, it has a few drawbacks. Here are a few disadvantages of this research method:

• Human error is a concern because the method depends on controlling variables. Improper implementation nullifies the validity of the research and conclusion.
• Eliminating extraneous variables (real-life scenarios) produces inaccurate conclusions.
• The process is time-consuming and expensive
• In medical research, it can have ethical implications by affecting patients’ well-being.
• Results are not descriptive and subjects can contribute to response bias.

Experimental research design is a sophisticated method that investigates relationships or occurrences among people or phenomena under a controlled environment and identifies the conditions responsible for such relationships or occurrences

Experimental research can be used in any industry to anticipate responses, changes, causes and effects. Here are some examples of experimental research :

• This research method can be used to evaluate employees’ skills. Organizations ask candidates to take tests before filling a post. It is used to screen qualified candidates from a pool of applicants. This allows organizations to identify skills at the time of employment. After training employees on the job, organizations further evaluate them to test impact and improvement. This is a pretest-posttest control group research example where employees are ‘subjects’ and the training is ‘treatment’.
• Educational institutions follow the pre-experimental research design to administer exams and evaluate students at the end of a semester. Students are the dependent variables and lectures are independent. Since exams are conducted at the end and not the beginning of a semester, it’s easy to conclude that it’s a one-shot case study research.
• To evaluate the teaching methods of two teachers, they can be assigned two student groups. After teaching their respective groups on the same topic, a posttest can determine which group scored better and who is better at teaching. This method can have its drawbacks as certain human factors, such as attitudes of students and effectiveness to grasp a subject, may negatively influence results. 

Experimental research is considered a standard method that uses observations, simulations and surveys to collect data. One of its unique features is the ability to control extraneous variables and their effects. It’s a suitable method for those looking to examine the relationship between cause and effect in a field setting or in a laboratory. Although experimental research design is a scientific approach, research is not entirely a scientific process. As much as managers need to know what is experimental research , they have to apply the correct research method, depending on the aim of the study.

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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• Questionnaire Design | Methods, Question Types & Examples
• Random Assignment in Experiments | Introduction & Examples
• Random vs. Systematic Error | Definition & Examples
• Reliability vs. Validity in Research | Difference, Types and Examples
• Reproducibility vs Replicability | Difference & Examples
• Reproducibility vs. Replicability | Difference & Examples
• Sampling Methods | Types, Techniques & Examples
• Semi-Structured Interview | Definition, Guide & Examples
• Simple Random Sampling | Definition, Steps & Examples
• Single, Double, & Triple Blind Study | Definition & Examples
• Stratified Sampling | Definition, Guide & Examples
• Structured Interview | Definition, Guide & Examples
• Survey Research | Definition, Examples & Methods
• Systematic Review | Definition, Example, & Guide
• Systematic Sampling | A Step-by-Step Guide with Examples
• Textual Analysis | Guide, 3 Approaches & Examples
• The 4 Types of Reliability in Research | Definitions & Examples
• The 4 Types of Validity in Research | Definitions & Examples
• Transcribing an Interview | 5 Steps & Transcription Software
• Triangulation in Research | Guide, Types, Examples
• Types of Interviews in Research | Guide & Examples
• Types of Research Designs Compared | Guide & Examples
• Types of Variables in Research & Statistics | Examples
• Unstructured Interview | Definition, Guide & Examples
• What Is a Case Study? | Definition, Examples & Methods
• What Is a Case-Control Study? | Definition & Examples
• What Is a Cohort Study? | Definition & Examples
• What Is a Conceptual Framework? | Tips & Examples
• What Is a Controlled Experiment? | Definitions & Examples
• What Is a Double-Barreled Question?
• What Is a Focus Group? | Step-by-Step Guide & Examples
• What Is a Likert Scale? | Guide & Examples
• What Is a Prospective Cohort Study? | Definition & Examples
• What Is a Retrospective Cohort Study? | Definition & Examples
• What Is Action Research? | Definition & Examples
• What Is an Observational Study? | Guide & Examples
• What Is Concurrent Validity? | Definition & Examples
• What Is Content Validity? | Definition & Examples
• What Is Convenience Sampling? | Definition & Examples
• What Is Convergent Validity? | Definition & Examples
• What Is Criterion Validity? | Definition & Examples
• What Is Data Cleansing? | Definition, Guide & Examples
• What Is Deductive Reasoning? | Explanation & Examples
• What Is Discriminant Validity? | Definition & Example
• What Is Ecological Validity? | Definition & Examples
• What Is Ethnography? | Definition, Guide & Examples
• What Is Face Validity? | Guide, Definition & Examples
• What Is Non-Probability Sampling? | Types & Examples
• What Is Participant Observation? | Definition & Examples
• What Is Peer Review? | Types & Examples
• What Is Predictive Validity? | Examples & Definition
• What Is Probability Sampling? | Types & Examples
• What Is Purposive Sampling? | Definition & Examples
• What Is Qualitative Observation? | Definition & Examples
• What Is Qualitative Research? | Methods & Examples
• What Is Quantitative Observation? | Definition & Examples
• What Is Quantitative Research? | Definition, Uses & Methods

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Chapter 10 Experimental Research

Experimental research, often considered to be the “gold standard” in research designs, is one of the most rigorous of all research designs. In this design, one or more independent variables are manipulated by the researcher (as treatments), subjects are randomly assigned to different treatment levels (random assignment), and the results of the treatments on outcomes (dependent variables) are observed. The unique strength of experimental research is its internal validity (causality) due to its ability to link cause and effect through treatment manipulation, while controlling for the spurious effect of extraneous variable.

Experimental research is best suited for explanatory research (rather than for descriptive or exploratory research), where the goal of the study is to examine cause-effect relationships. It also works well for research that involves a relatively limited and well-defined set of independent variables that can either be manipulated or controlled. Experimental research can be conducted in laboratory or field settings. Laboratory experiments , conducted in laboratory (artificial) settings, tend to be high in internal validity, but this comes at the cost of low external validity (generalizability), because the artificial (laboratory) setting in which the study is conducted may not reflect the real world. Field experiments , conducted in field settings such as in a real organization, and high in both internal and external validity. But such experiments are relatively rare, because of the difficulties associated with manipulating treatments and controlling for extraneous effects in a field setting.

Experimental research can be grouped into two broad categories: true experimental designs and quasi-experimental designs. Both designs require treatment manipulation, but while true experiments also require random assignment, quasi-experiments do not. Sometimes, we also refer to non-experimental research, which is not really a research design, but an all-inclusive term that includes all types of research that do not employ treatment manipulation or random assignment, such as survey research, observational research, and correlational studies.

Basic Concepts

Treatment and control groups. In experimental research, some subjects are administered one or more experimental stimulus called a treatment (the treatment group ) while other subjects are not given such a stimulus (the control group ). The treatment may be considered successful if subjects in the treatment group rate more favorably on outcome variables than control group subjects. Multiple levels of experimental stimulus may be administered, in which case, there may be more than one treatment group. For example, in order to test the effects of a new drug intended to treat a certain medical condition like dementia, if a sample of dementia patients is randomly divided into three groups, with the first group receiving a high dosage of the drug, the second group receiving a low dosage, and the third group receives a placebo such as a sugar pill (control group), then the first two groups are experimental groups and the third group is a control group. After administering the drug for a period of time, if the condition of the experimental group subjects improved significantly more than the control group subjects, we can say that the drug is effective. We can also compare the conditions of the high and low dosage experimental groups to determine if the high dose is more effective than the low dose.

Treatment manipulation. Treatments are the unique feature of experimental research that sets this design apart from all other research methods. Treatment manipulation helps control for the “cause” in cause-effect relationships. Naturally, the validity of experimental research depends on how well the treatment was manipulated. Treatment manipulation must be checked using pretests and pilot tests prior to the experimental study. Any measurements conducted before the treatment is administered are called pretest measures , while those conducted after the treatment are posttest measures .

Random selection and assignment. Random selection is the process of randomly drawing a sample from a population or a sampling frame. This approach is typically employed in survey research, and assures that each unit in the population has a positive chance of being selected into the sample. Random assignment is however a process of randomly assigning subjects to experimental or control groups. This is a standard practice in true experimental research to ensure that treatment groups are similar (equivalent) to each other and to the control group, prior to treatment administration. Random selection is related to sampling, and is therefore, more closely related to the external validity (generalizability) of findings. However, random assignment is related to design, and is therefore most related to internal validity. It is possible to have both random selection and random assignment in well-designed experimental research, but quasi-experimental research involves neither random selection nor random assignment.

Threats to internal validity. Although experimental designs are considered more rigorous than other research methods in terms of the internal validity of their inferences (by virtue of their ability to control causes through treatment manipulation), they are not immune to internal validity threats. Some of these threats to internal validity are described below, within the context of a study of the impact of a special remedial math tutoring program for improving the math abilities of high school students.

• History threat is the possibility that the observed effects (dependent variables) are caused by extraneous or historical events rather than by the experimental treatment. For instance, students’ post-remedial math score improvement may have been caused by their preparation for a math exam at their school, rather than the remedial math program.
• Maturation threat refers to the possibility that observed effects are caused by natural maturation of subjects (e.g., a general improvement in their intellectual ability to understand complex concepts) rather than the experimental treatment.
• Testing threat is a threat in pre-post designs where subjects’ posttest responses are conditioned by their pretest responses. For instance, if students remember their answers from the pretest evaluation, they may tend to repeat them in the posttest exam. Not conducting a pretest can help avoid this threat.
• Instrumentation threat , which also occurs in pre-post designs, refers to the possibility that the difference between pretest and posttest scores is not due to the remedial math program, but due to changes in the administered test, such as the posttest having a higher or lower degree of difficulty than the pretest.
• Mortality threat refers to the possibility that subjects may be dropping out of the study at differential rates between the treatment and control groups due to a systematic reason, such that the dropouts were mostly students who scored low on the pretest. If the low-performing students drop out, the results of the posttest will be artificially inflated by the preponderance of high-performing students.
• Regression threat , also called a regression to the mean, refers to the statistical tendency of a group’s overall performance on a measure during a posttest to regress toward the mean of that measure rather than in the anticipated direction. For instance, if subjects scored high on a pretest, they will have a tendency to score lower on the posttest (closer to the mean) because their high scores (away from the mean) during the pretest was possibly a statistical aberration. This problem tends to be more prevalent in non-random samples and when the two measures are imperfectly correlated.

Two-Group Experimental Designs

The simplest true experimental designs are two group designs involving one treatment group and one control group, and are ideally suited for testing the effects of a single independent variable that can be manipulated as a treatment. The two basic two-group designs are the pretest-posttest control group design and the posttest-only control group design, while variations may include covariance designs. These designs are often depicted using a standardized design notation, where R represents random assignment of subjects to groups, X represents the treatment administered to the treatment group, and O represents pretest or posttest observations of the dependent variable (with different subscripts to distinguish between pretest and posttest observations of treatment and control groups).

Pretest-posttest control group design . In this design, subjects are randomly assigned to treatment and control groups, subjected to an initial (pretest) measurement of the dependent variables of interest, the treatment group is administered a treatment (representing the independent variable of interest), and the dependent variables measured again (posttest). The notation of this design is shown in Figure 10.1.

Figure 10.1. Pretest-posttest control group design

The effect E of the experimental treatment in the pretest posttest design is measured as the difference in the posttest and pretest scores between the treatment and control groups:

E = (O 2 – O 1 ) – (O 4 – O 3 )

Statistical analysis of this design involves a simple analysis of variance (ANOVA) between the treatment and control groups. The pretest posttest design handles several threats to internal validity, such as maturation, testing, and regression, since these threats can be expected to influence both treatment and control groups in a similar (random) manner. The selection threat is controlled via random assignment. However, additional threats to internal validity may exist. For instance, mortality can be a problem if there are differential dropout rates between the two groups, and the pretest measurement may bias the posttest measurement (especially if the pretest introduces unusual topics or content).

Posttest-only control group design . This design is a simpler version of the pretest-posttest design where pretest measurements are omitted. The design notation is shown in Figure 10.2.

Figure 10.2. Posttest only control group design.

The treatment effect is measured simply as the difference in the posttest scores between the two groups:

E = (O 1 – O 2 )

The appropriate statistical analysis of this design is also a two- group analysis of variance (ANOVA). The simplicity of this design makes it more attractive than the pretest-posttest design in terms of internal validity. This design controls for maturation, testing, regression, selection, and pretest-posttest interaction, though the mortality threat may continue to exist.

Covariance designs . Sometimes, measures of dependent variables may be influenced by extraneous variables called covariates . Covariates are those variables that are not of central interest to an experimental study, but should nevertheless be controlled in an experimental design in order to eliminate their potential effect on the dependent variable and therefore allow for a more accurate detection of the effects of the independent variables of interest. The experimental designs discussed earlier did not control for such covariates. A covariance design (also called a concomitant variable design) is a special type of pretest posttest control group design where the pretest measure is essentially a measurement of the covariates of interest rather than that of the dependent variables. The design notation is shown in Figure 10.3, where C represents the covariates:

Figure 10.3. Covariance design

Because the pretest measure is not a measurement of the dependent variable, but rather a covariate, the treatment effect is measured as the difference in the posttest scores between the treatment and control groups as:

Figure 10.4. 2 x 2 factorial design

Factorial designs can also be depicted using a design notation, such as that shown on the right panel of Figure 10.4. R represents random assignment of subjects to treatment groups, X represents the treatment groups themselves (the subscripts of X represents the level of each factor), and O represent observations of the dependent variable. Notice that the 2 x 2 factorial design will have four treatment groups, corresponding to the four combinations of the two levels of each factor. Correspondingly, the 2 x 3 design will have six treatment groups, and the 2 x 2 x 2 design will have eight treatment groups. As a rule of thumb, each cell in a factorial design should have a minimum sample size of 20 (this estimate is derived from Cohen’s power calculations based on medium effect sizes). So a 2 x 2 x 2 factorial design requires a minimum total sample size of 160 subjects, with at least 20 subjects in each cell. As you can see, the cost of data collection can increase substantially with more levels or factors in your factorial design. Sometimes, due to resource constraints, some cells in such factorial designs may not receive any treatment at all, which are called incomplete factorial designs . Such incomplete designs hurt our ability to draw inferences about the incomplete factors.

In a factorial design, a main effect is said to exist if the dependent variable shows a significant difference between multiple levels of one factor, at all levels of other factors. No change in the dependent variable across factor levels is the null case (baseline), from which main effects are evaluated. In the above example, you may see a main effect of instructional type, instructional time, or both on learning outcomes. An interaction effect exists when the effect of differences in one factor depends upon the level of a second factor. In our example, if the effect of instructional type on learning outcomes is greater for 3 hours/week of instructional time than for 1.5 hours/week, then we can say that there is an interaction effect between instructional type and instructional time on learning outcomes. Note that the presence of interaction effects dominate and make main effects irrelevant, and it is not meaningful to interpret main effects if interaction effects are significant.

Hybrid Experimental Designs

Hybrid designs are those that are formed by combining features of more established designs. Three such hybrid designs are randomized bocks design, Solomon four-group design, and switched replications design.

Randomized block design. This is a variation of the posttest-only or pretest-posttest control group design where the subject population can be grouped into relatively homogeneous subgroups (called blocks ) within which the experiment is replicated. For instance, if you want to replicate the same posttest-only design among university students and full -time working professionals (two homogeneous blocks), subjects in both blocks are randomly split between treatment group (receiving the same treatment) or control group (see Figure 10.5). The purpose of this design is to reduce the “noise” or variance in data that may be attributable to differences between the blocks so that the actual effect of interest can be detected more accurately.

Figure 10.5. Randomized blocks design.

Solomon four-group design . In this design, the sample is divided into two treatment groups and two control groups. One treatment group and one control group receive the pretest, and the other two groups do not. This design represents a combination of posttest-only and pretest-posttest control group design, and is intended to test for the potential biasing effect of pretest measurement on posttest measures that tends to occur in pretest-posttest designs but not in posttest only designs. The design notation is shown in Figure 10.6.

Figure 10.6. Solomon four-group design

Switched replication design . This is a two-group design implemented in two phases with three waves of measurement. The treatment group in the first phase serves as the control group in the second phase, and the control group in the first phase becomes the treatment group in the second phase, as illustrated in Figure 10.7. In other words, the original design is repeated or replicated temporally with treatment/control roles switched between the two groups. By the end of the study, all participants will have received the treatment either during the first or the second phase. This design is most feasible in organizational contexts where organizational programs (e.g., employee training) are implemented in a phased manner or are repeated at regular intervals.

Figure 10.7. Switched replication design.

Quasi-Experimental Designs

Quasi-experimental designs are almost identical to true experimental designs, but lacking one key ingredient: random assignment. For instance, one entire class section or one organization is used as the treatment group, while another section of the same class or a different organization in the same industry is used as the control group. This lack of random assignment potentially results in groups that are non-equivalent, such as one group possessing greater mastery of a certain content than the other group, say by virtue of having a better teacher in a previous semester, which introduces the possibility of selection bias . Quasi-experimental designs are therefore inferior to true experimental designs in interval validity due to the presence of a variety of selection related threats such as selection-maturation threat (the treatment and control groups maturing at different rates), selection-history threat (the treatment and control groups being differentially impact by extraneous or historical events), selection-regression threat (the treatment and control groups regressing toward the mean between pretest and posttest at different rates), selection-instrumentation threat (the treatment and control groups responding differently to the measurement), selection-testing (the treatment and control groups responding differently to the pretest), and selection-mortality (the treatment and control groups demonstrating differential dropout rates). Given these selection threats, it is generally preferable to avoid quasi-experimental designs to the greatest extent possible.

Many true experimental designs can be converted to quasi-experimental designs by omitting random assignment. For instance, the quasi-equivalent version of pretest-posttest control group design is called nonequivalent groups design (NEGD), as shown in Figure 10.8, with random assignment R replaced by non-equivalent (non-random) assignment N . Likewise, the quasi -experimental version of switched replication design is called non-equivalent switched replication design (see Figure 10.9).

Figure 10.8. NEGD design.

Figure 10.9. Non-equivalent switched replication design.

In addition, there are quite a few unique non -equivalent designs without corresponding true experimental design cousins. Some of the more useful of these designs are discussed next.

Regression-discontinuity (RD) design . This is a non-equivalent pretest-posttest design where subjects are assigned to treatment or control group based on a cutoff score on a preprogram measure. For instance, patients who are severely ill may be assigned to a treatment group to test the efficacy of a new drug or treatment protocol and those who are mildly ill are assigned to the control group. In another example, students who are lagging behind on standardized test scores may be selected for a remedial curriculum program intended to improve their performance, while those who score high on such tests are not selected from the remedial program. The design notation can be represented as follows, where C represents the cutoff score:

Figure 10.10. RD design.

Because of the use of a cutoff score, it is possible that the observed results may be a function of the cutoff score rather than the treatment, which introduces a new threat to internal validity. However, using the cutoff score also ensures that limited or costly resources are distributed to people who need them the most rather than randomly across a population, while simultaneously allowing a quasi-experimental treatment. The control group scores in the RD design does not serve as a benchmark for comparing treatment group scores, given the systematic non-equivalence between the two groups. Rather, if there is no discontinuity between pretest and posttest scores in the control group, but such a discontinuity persists in the treatment group, then this discontinuity is viewed as evidence of the treatment effect.

Proxy pretest design . This design, shown in Figure 10.11, looks very similar to the standard NEGD (pretest-posttest) design, with one critical difference: the pretest score is collected after the treatment is administered. A typical application of this design is when a researcher is brought in to test the efficacy of a program (e.g., an educational program) after the program has already started and pretest data is not available. Under such circumstances, the best option for the researcher is often to use a different prerecorded measure, such as students’ grade point average before the start of the program, as a proxy for pretest data. A variation of the proxy pretest design is to use subjects’ posttest recollection of pretest data, which may be subject to recall bias, but nevertheless may provide a measure of perceived gain or change in the dependent variable.

Figure 10.11. Proxy pretest design.

Separate pretest-posttest samples design . This design is useful if it is not possible to collect pretest and posttest data from the same subjects for some reason. As shown in Figure 10.12, there are four groups in this design, but two groups come from a single non-equivalent group, while the other two groups come from a different non-equivalent group. For instance, you want to test customer satisfaction with a new online service that is implemented in one city but not in another. In this case, customers in the first city serve as the treatment group and those in the second city constitute the control group. If it is not possible to obtain pretest and posttest measures from the same customers, you can measure customer satisfaction at one point in time, implement the new service program, and measure customer satisfaction (with a different set of customers) after the program is implemented. Customer satisfaction is also measured in the control group at the same times as in the treatment group, but without the new program implementation. The design is not particularly strong, because you cannot examine the changes in any specific customer’s satisfaction score before and after the implementation, but you can only examine average customer satisfaction scores. Despite the lower internal validity, this design may still be a useful way of collecting quasi-experimental data when pretest and posttest data are not available from the same subjects.

Figure 10.12. Separate pretest-posttest samples design.

Nonequivalent dependent variable (NEDV) design . This is a single-group pre-post quasi-experimental design with two outcome measures, where one measure is theoretically expected to be influenced by the treatment and the other measure is not. For instance, if you are designing a new calculus curriculum for high school students, this curriculum is likely to influence students’ posttest calculus scores but not algebra scores. However, the posttest algebra scores may still vary due to extraneous factors such as history or maturation. Hence, the pre-post algebra scores can be used as a control measure, while that of pre-post calculus can be treated as the treatment measure. The design notation, shown in Figure 10.13, indicates the single group by a single N , followed by pretest O 1 and posttest O 2 for calculus and algebra for the same group of students. This design is weak in internal validity, but its advantage lies in not having to use a separate control group.

An interesting variation of the NEDV design is a pattern matching NEDV design , which employs multiple outcome variables and a theory that explains how much each variable will be affected by the treatment. The researcher can then examine if the theoretical prediction is matched in actual observations. This pattern-matching technique, based on the degree of correspondence between theoretical and observed patterns is a powerful way of alleviating internal validity concerns in the original NEDV design.

Figure 10.13. NEDV design.

Perils of Experimental Research

Experimental research is one of the most difficult of research designs, and should not be taken lightly. This type of research is often best with a multitude of methodological problems. First, though experimental research requires theories for framing hypotheses for testing, much of current experimental research is atheoretical. Without theories, the hypotheses being tested tend to be ad hoc, possibly illogical, and meaningless. Second, many of the measurement instruments used in experimental research are not tested for reliability and validity, and are incomparable across studies. Consequently, results generated using such instruments are also incomparable. Third, many experimental research use inappropriate research designs, such as irrelevant dependent variables, no interaction effects, no experimental controls, and non-equivalent stimulus across treatment groups. Findings from such studies tend to lack internal validity and are highly suspect. Fourth, the treatments (tasks) used in experimental research may be diverse, incomparable, and inconsistent across studies and sometimes inappropriate for the subject population. For instance, undergraduate student subjects are often asked to pretend that they are marketing managers and asked to perform a complex budget allocation task in which they have no experience or expertise. The use of such inappropriate tasks, introduces new threats to internal validity (i.e., subject’s performance may be an artifact of the content or difficulty of the task setting), generates findings that are non-interpretable and meaningless, and makes integration of findings across studies impossible.

The design of proper experimental treatments is a very important task in experimental design, because the treatment is the raison d’etre of the experimental method, and must never be rushed or neglected. To design an adequate and appropriate task, researchers should use prevalidated tasks if available, conduct treatment manipulation checks to check for the adequacy of such tasks (by debriefing subjects after performing the assigned task), conduct pilot tests (repeatedly, if necessary), and if doubt, using tasks that are simpler and familiar for the respondent sample than tasks that are complex or unfamiliar.

In summary, this chapter introduced key concepts in the experimental design research method and introduced a variety of true experimental and quasi-experimental designs. Although these designs vary widely in internal validity, designs with less internal validity should not be overlooked and may sometimes be useful under specific circumstances and empirical contingencies.

• Social Science Research: Principles, Methods, and Practices. Authored by : Anol Bhattacherjee. Provided by : University of South Florida. Located at : http://scholarcommons.usf.edu/oa_textbooks/3/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

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Experimental vs Observational Studies: Differences & Examples

Understanding the differences between experimental vs observational studies is crucial for interpreting findings and drawing valid conclusions. Both methodologies are used extensively in various fields, including medicine, social sciences, and environmental studies. 

Researchers often use observational and experimental studies to gather comprehensive data and draw robust conclusions about their investigating phenomena. 

This blog post will explore what makes these two types of studies unique, their fundamental differences, and examples to illustrate their applications.

What is an Experimental Study?

An experimental study is a research design in which the investigator actively manipulates one or more variables to observe their effect on another variable. This type of study often takes place in a controlled environment, which allows researchers to establish cause-and-effect relationships.

Key Characteristics of Experimental Studies:

• Manipulation: Researchers manipulate the independent variable(s).
• Control: Other variables are kept constant to isolate the effect of the independent variable.
• Randomization: Subjects are randomly assigned to different groups to minimize bias.
• Replication: The study can be replicated to verify results.

Types of Experimental Study

• Laboratory Experiments: Conducted in a controlled environment where variables can be precisely controlled.
• Field Research : These are conducted in a natural setting but still involve manipulation and control of variables.
• Clinical Trials: Used in medical research and the healthcare industry to test the efficacy of new treatments or drugs.

Example of an Experimental Study:

Imagine a study to test the effectiveness of a new drug for reducing blood pressure. Researchers would:

• Randomly assign participants to two groups: receiving the drug and receiving a placebo.
• Ensure that participants do not know their group (double-blind procedure).
• Measure blood pressure before and after the intervention.
• Compare the changes in blood pressure between the two groups to determine the drug’s effectiveness.

What is an Observational Study?

An observational study is a research design in which the investigator observes subjects and measures variables without intervening or manipulating the study environment. This type of study is often used when manipulating impractical or unethical variables.

Key Characteristics of Observational Studies:

• No Manipulation: Researchers do not manipulate the independent variable.
• Natural Setting: Observations are made in a natural environment.
• Causation Limitations: It is difficult to establish cause-and-effect relationships due to the need for more control over variables.
• Descriptive: Often used to describe characteristics or outcomes.

Types of Observational Studies: 

• Cohort Studies : Follow a control group of people over time to observe the development of outcomes.
• Case-Control Studies: Compare individuals with a specific outcome (cases) to those without (controls) to identify factors that might contribute to the outcome.
• Cross-Sectional Studies : Collect data from a population at a single point to analyze the prevalence of an outcome or characteristic.

Example of an Observational Study:

Consider a study examining the relationship between smoking and lung cancer. Researchers would:

• Identify a cohort of smokers and non-smokers.
• Follow both groups over time to record incidences of lung cancer.
• Analyze the data to observe any differences in cancer rates between smokers and non-smokers.

Difference Between Experimental vs Observational Studies

Choosing Between Experimental and Observational Studies

The researchers relied on statistical analysis to interpret the results of randomized controlled trials, building upon the foundations established by prior research.

Use Experimental Studies When:

• Causality is Important: If determining a cause-and-effect relationship is crucial, experimental studies are the way to go.
• Variables Can Be Controlled: When you can manipulate and control the variables in a lab or controlled setting, experimental studies are suitable.
• Randomization is Possible: When random assignment of subjects is feasible and ethical, experimental designs are appropriate.

Use Observational Studies When:

• Ethical Concerns Exist: If manipulating variables is unethical, such as exposing individuals to harmful substances, observational studies are necessary.
• Practical Constraints Apply: When experimental studies are impractical due to cost or logistics, observational studies can be a viable alternative.
• Natural Settings Are Required: If studying phenomena in their natural environment is essential, observational studies are the right choice.

Strengths and Limitations

Experimental studies.

• Establish Causality: Experimental studies can establish causal relationships between variables by controlling and using randomization.
• Control Over Confounding Variables: The controlled environment allows researchers to minimize the influence of external variables that might skew results.
• Repeatability: Experiments can often be repeated to verify results and ensure consistency.

Limitations:

• Ethical Concerns: Manipulating variables may be unethical in certain situations, such as exposing individuals to harmful conditions.
• Artificial Environment: The controlled setting may not reflect real-world conditions, potentially affecting the generalizability of results.
• Cost and Complexity: Experimental studies can be costly and logistically complex, especially with large sample sizes.

Observational Studies

• Real-World Insights: Observational studies provide valuable insights into how variables interact in natural settings.
• Ethical and Practical: These studies avoid ethical concerns associated with manipulation and can be more practical regarding cost and time.
• Diverse Applications: Observational studies can be used in various fields and situations where experiments are not feasible.
• Lack of Causality: It’s easier to establish causation with manipulation, and results are limited to identifying correlations.
• Potential for Confounding: Uncontrolled external variables may influence the results, leading to biased conclusions.
• Observer Bias: Researchers may unintentionally influence outcomes through their expectations or interpretations of data.

Examples in Various Fields

• Experimental Study: Clinical trials testing the effectiveness of a new drug against a placebo to determine its impact on patient recovery.
• Observational Study: Studying the dietary habits of different populations to identify potential links between nutrition and disease prevalence.
• Experimental Study: Conducting a lab experiment to test the effect of sleep deprivation on cognitive performance by controlling sleep hours and measuring test scores.
• Observational Study: Observing social interactions in a public setting to explore natural communication patterns without intervention.

Environmental Science

• Experimental Study: Testing the impact of a specific pollutant on plant growth in a controlled greenhouse setting.
• Observational Study: Monitoring wildlife populations in a natural habitat to assess the effects of climate change on species distribution.

How QuestionPro Research Can Help in Experimental vs Observational Studies

Choosing between experimental and observational studies is a critical decision that can significantly impact the outcomes and interpretations of a study. QuestionPro Research offers powerful tools and features that can enhance both types of studies, giving researchers the flexibility and capability to gather, analyze, and interpret data effectively.

Enhancing Experimental Studies with QuestionPro

Experimental studies require a high degree of control over variables, randomization, and, often, repeated trials to establish causal relationships. QuestionPro excels in facilitating these requirements through several key features:

• Survey Design and Distribution: With QuestionPro, researchers can design intricate surveys tailored to their experimental needs. The platform supports random assignment of participants to different groups, ensuring unbiased distribution and enhancing the study’s validity.
• Data Collection and Management: Real-time data collection and management tools allow researchers to monitor responses as they come in. This is crucial for experimental studies where data collection timing and sequence can impact the results.
• Advanced Analytics: QuestionPro offers robust analytical tools that can handle complex data sets, enabling researchers to conduct in-depth statistical analyses to determine the effects of the experimental interventions.

Supporting Observational Studies with QuestionPro

Observational studies involve gathering data without manipulating variables, focusing on natural settings and real-world scenarios. QuestionPro’s capabilities are well-suited for these studies as well:

• Customizable Surveys: Researchers can create detailed surveys to capture a wide range of observational data. QuestionPro’s customizable templates and question types allow for flexibility in capturing nuanced information.
• Mobile Data Collection: For field research, QuestionPro’s mobile app enables data collection on the go, making it easier to conduct studies in diverse settings without internet connectivity.
• Longitudinal Data Tracking: Observational studies often require data collection over extended periods. QuestionPro’s platform supports longitudinal studies, allowing researchers to track changes and trends.

Experimental and observational studies are essential tools in the researcher’s toolkit. Each serves a unique purpose and offers distinct advantages and limitations. By understanding their differences, researchers can choose the most appropriate study design for their specific objectives, ensuring their findings are valid and applicable to real-world situations.

Whether establishing causality through experimental studies or exploring correlations with observational research designs, the insights gained from these methodologies continue to shape our understanding of the world around us. 

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What you need to know about the updated 2024-2025 COVID-19 vaccine

Assistant Director of Pharmacy – Medication Safety and Policy Ohio State Wexner Medical Center

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The federal Centers for Disease Control and Prevention recommends everyone age 6 months and older receive an updated COVID-19 vaccine formulation for 2024-2025.

You should get the updated vaccine whether or not you’ve ever received a COVID-19 vaccine or vaccine dose in the past. You should also receive the vaccine regardless of whether you’ve ever had a COVID-19 infection.

The COVID-19 vaccine can be given at the same time as other vaccines, including the 2024-2025 influenza (flu) vaccine , so it may be convenient to get both during the same visit.

Why is an updated COVID-19 vaccine recommended?

The COVID-19 virus is always changing, and new variants will circulate depending on the year and season. The updated vaccines will target the variants that are currently of highest concern and boost immune response to the virus.

Expect reformulated vaccines to become routine, similar to flu vaccines.

Where can I get the updated COVID-19 vaccine?

Updated COVID-19 vaccines are being provided by the Pfizer, Moderna and Novavax companies. The Ohio State University Wexner Medical Center will offer the vaccines, as will retail pharmacies, as they become available.

Why should I get the updated COVID-19 vaccine?

Vaccines help reduce the likelihood of person-to-person transmission  of COVID-19. If you do become infected, being vaccinated reduces the severity of symptoms and the risk of hospitalization.

Also, because long COVID  continues to be a concern for people previously infected with the virus, the 2024-2025 COVID-19 vaccine is an effective tool to protect oneself.

When is the best time to get the updated COVID-19 vaccine?

Although administration earlier in the season will provide the most benefit, it’s never too late to receive the vaccine (that is, until another formulation is announced). Since its discovery, the COVID-19 virus has always been circulating and continues to circulate.

Who should not get the COVID-19 vaccine?

Check with your medical provider before getting a COVID-19 vaccine if you:

• Have had a prior allergic reaction to a COVID-19 vaccine
• Have a contraindication to any ingredient of the vaccine
• Have had a serious adverse/negative reaction to a prior vaccine
• Have or have had myocarditis  (inflammation of the heart muscle) or pericarditis (inflammation of the lining outside of the heart)
• Have had multisystem inflammatory syndrome

If you are moderate to severely immunocompromised and are receiving the pemivibart (Pemgarda) infusion, check with your medical provider — there could be potential issues with vaccine timing around infusion doses.

How long does it take for the updated COVID-19 vaccine to provide protection?

Vaccines, including those for COVID-19, must stimulate the immune system to ramp up against proteins unique to the virus. It will take one to two weeks to develop full effects, although your body will start working on its defense right away.

How long will the COVID-19 vaccine provide protection?

Unfortunately, the COVID-19 vaccine’s protection declines over time. The best protection lasts through four to six months. With new variants emerging seasonally and the quick loss of protection, it’s important to get a new vaccine each season as recommended.

What are possible side effects of the updated COVID-19 vaccine?

People who get the vaccine may experience temporary viral symptoms, feeling generally unwell and having chills and possible fever. There could also be tenderness at the injection site for a few days. These side effects are similar to those from earlier COVID-19 vaccines.

If I’m ill, should I wait to receive the COVID-19 vaccine?

If you are moderately or severely ill, or you’re confirmed to have an active COVID-19 infection, you should wait to get the vaccine. Wait for the infection to resolve and until you’re outside the isolation requirement window before receiving a vaccine.

Will I only need to be vaccinated against COVID-19 one time this year?

People without any risk factors or immunocompromising conditions will only need one COVID-19 vaccine this year.

Some special populations may have additional recommendations per the CDC. Check with your health care provider.

How much will the vaccine cost?

The COVID-19 vaccine is available for free to most patients with private health insurance, Medicare or Medicaid plans. For those without health insurance, check with your local or state health department for potential options for free vaccines. The CDC’s Bridge Access Program, which provided some free COVID-19 vaccines for patients who are uninsured, ended in August 2024.

The first step in the journey to your best health begins with a primary care provider who cares

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• Open access
• Published: 03 September 2024

Selection of reference genes for expression profiling in biostimulation research of soybean

• Magdalena Sozoniuk 1 ,
• Michał Świeca 2 ,
• Andrea Bohatá 3 ,
• Petr Bartoš 4 ,
• Jan Bedrníček 5 ,
• František Lorenc 5 ,
• Markéta Jarošová 3 ,
• Kristýna Perná 6 ,
• Adéla Stupková 3 ,
• Jana Lencová 3 ,
• Pavel Olšan 4 ,
• Jan Bárta 3 ,
• Agnieszka Szparaga 7 , 9 ,
• María Cecilia Pérez-Pizá 10 &
• Sławomir Kocira 8 , 9  

Chemical and Biological Technologies in Agriculture volume  11 , Article number:  130 ( 2024 ) Cite this article

Metrics details

Plant biostimulants constitute a promising environmentally friendly alternative for increasing crop yield and tolerance to unfavorable conditions. Among various types of such formulations, botanical extracts are gaining more recognition as products supporting plant performance. Moreover, novel tools such as cold-plasma or low-pressure microwave plasma discharge are being proposed as techniques that might improve their efficacy. Elucidation of the biostimulant’s mode of action requires complex research at a molecular level. Transcriptional changes occurring after biostimulant spraying might be investigated using RT-qPCR. However, this technique requires data normalization against stable endogenous controls.

Here, we tested the expression stability of ten candidate genes in soybean plants exposed to various biostimulants treatment. Selection of the best-performing reference genes was conducted using four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method). According to the obtained results, Bic-C2 (RNA-binding protein Bicaudal-C) and CYP (cyclophilin type peptidyl-prolyl cis–trans isomerase) showed highest expression stability, while expression of EF1B (elongation factor 1-beta) fluctuated the most among a tested set of candidate genes.

Conclusions

Overall, we recommend using Bic-C2 together with CYP for the RT-qPCR data normalization in soybean biostimulation experiments. To our best knowledge, this is the first comprehensive study of reference genes stability in plants subjected to biostimulant treatment. The results of this study will aid in further biostimulant research in crop plants, facilitating analyses performed on the transcriptional level.

Graphical Abstract

Changing climate conditions and growing world population require novel solutions to meet the demand for food and feed production without negatively affecting the environment. Various strategies are being implemented to overcome these problems—one of them is the application of biostimulants, which constitute a range of formulations based on natural products used to promote the growth and stress tolerance of crop plants [ 1 ]. The main categories of biostimulants comprise humic substances, protein hydrolysates, beneficial fungi and bacteria, chitosan and other biopolymers, seaweed extracts, and botanical extracts [ 2 ]. Compared to others, botanical extracts’ mechanism of action is much less characterized. Plant-based biostimulants are rich in biologically active compounds, such as different phytohormones, antioxidants, vitamins, and other secondary metabolites, which improve overall plant performance by acting on various levels and through different pathways [ 3 ]. The characterization of these complex multilayer interactions requires more research employing omics tools (transcriptomics, proteomics, metabolomics, and phenomics) [ 4 ]. Yet, despite us not totally understanding how, botanical extracts are effective in stimulating crop growth and development under both optimal [ 5 , 6 , 7 ] and stress conditions [ 8 , 9 , 10 ].

Horsetail ( Equisetum arvense L.), dog rose ( Rosa canina L.), and common soapwort ( Saponaria officinalis L.) are plants widely known for their therapeutic effects promoted by various bioactive compounds. Horsetail is distinguished by its high content of silicon within its aerial parts [ 11 ], which can be useful in two ways, including improved absorption of liquid biostimulant by treated plant and induce the metabolic response of treated plant by the microscopic disruption of its tissues by silicon particles. Moreover, horsetail extract can exhibit antifungal properties [ 12 ] and thus can serve as the contact fungicide in the biostimulant. Rosehip, as the fruit of dog rose, is a rich source of antioxidants, especially vitamin C and polyphenolic compounds [ 13 ]. Its role in biostimulants can lie in increasing the antioxidant status of treated plants and improving the stability of biostimulants. Soapwort is characterized by a high content of saponins and their glycosides [ 14 ]. These compounds are known for their potential toxicity, so their application in biostimulants should be well considered. However, they represent non-ionic biosurfactants with excellent performance [ 15 ]. In general, the presence of a surfactant agent as a detergent adjuvant is important for the optimal formulation of agrochemicals, leading to better adhesion on the surface of plants [ 16 ]. Except for this, biosurfactants may be applied in plant disease and pest control, boost plant growth through microbial interaction and enhance plant immunity [ 17 ]. Concisely, the specific properties of each of these plants predetermine their application within the complex biostimulant.

Since the use of biostimulants has been recognized as a viable method for enhancing crop resilience and yield, scientists are looking for novel ways of improving their mode of action [ 3 , 18 ]. A recently proposed strategy involves cold-plasma activation of plant-based extracts, including gliding arc plasma discharge [ 19 ]. Thus far, non-thermal plasma technology has been applied in agriculture mainly for seed treatment in terms of microbial inactivation and germination improvement [ 20 , 21 ]. Another reported strategy is using plasma-activated water to enhance plants’ growth and tolerance to abiotic and biotic stresses [ 22 , 23 ]. This is attributed to the generation of a mixture of reactive oxygen and nitrogen species during plasma discharges, some of which act as signaling molecules in cells and activate plants’ defense systems [ 24 ]. Coupling plant-based biostimulants with cold-plasma activation is an innovative approach that has a high potential for improving crop yield in future [ 19 ]. Another alternative way that might influence the effectiveness of plant-derived biostimulants is the employment of microwave plasma discharge. This type of plasma treatment can operate under atmospheric pressure so that it can be simply used for the treatment of various biological materials. Along with decontamination [ 25 ] or degradation of hazardous compounds, including mycotoxins [ 26 ], microwave plasma discharge can be used to improve the extractability of bioactive compounds from plant materials [ 27 ]. The inhibition of adverse enzymatic degradation of plant materials was reported after their treatment by microwave plasma discharge [ 26 ]. The pretreatment of dried herbs by plasma discharge may potentially improve the chemical properties and stability of derived water extracts and biostimulants, respectively.

Nevertheless, it should be emphasized that the mechanisms underlying the plants’ response to such novel biostimulants have not yet been elucidated. Analysis of transcriptional changes occurring after biostimulant treatment might provide insights into its mode of action. The RT-qPCR technique allows the examination of the expression profiles of various genes related, for instance, to plant redox homeostasis and defense responses, which might be involved in the process. However, this technique is sensitive to various experimental inaccuracies occurring during analysis such as differences in sample quality and quantity, RNA integrity, reverse transcription efficiency, dilution preparation, and pipetting errors. To correct for such non-biological variations, proper data normalization using reference genes (RGs) showing stable expression in tested material are required [ 28 , 29 ]. Since many studies report spatiotemporal variation in the expression of commonly used reference genes, identification of reliable endogenous controls should precede analyses of genes of interest in every RT-qPCR experiment [ 28 ].

Here, we tested the expression stability of ten candidate reference genes in soybean plants sprayed with three different variants of novel plant-based biostimulants. Two variants of biostimulant were formulated using either the gliding arc plasma or low-pressure microwave plasma discharge. To our best knowledge, this is the first report on the identification of reference genes in biostimulant-treated soybean. The results of this study will aid in further biostimulant research in crop plants, facilitating analyses performed on the transcriptional level.

Materials and methods

Preparation of biostimulants.

First, three different biostimulants were prepared. The biostimulants were made from the mixture of dried and milled field horsetail ( Equisetum arvense L.) stems and branches, dog rose ( Rosa canina L.) fruits and soapwort ( Saponaria officinalis L.) roots in the following ratio ( w / w ): 95.3%: 4.6%: 0.1%, respectively. The biostimulants differed according to plasma treatment used. Untreated biostimulant (without plasma application) was prepared as follows: 25 g of the herbal mixture was mixed with 250 ml of water and then extracted at 100 °C for 30 min. Second type of biostimulant was prepared in the same manner as the control biostimulant but was subsequently treated with gliding arc (GA) atmospheric plasma discharge for 30 s with air as a working gas at a flow rate of 30 standard cubic feet per hour. In the case of the third type of the biostimulant, microwave plasma discharge [ 30 ] was applied (500 W for 30 s) on the solid herbal mixture. After the MW plasma treatment, the liquid extract was obtained under the same extraction conditions as mentioned above. Pure water served as control. The biostimulants preparation procedure is depicted in Supplementary Figure S1.

Experimental design of biostimulant application on soybean plants

The experimental material consisted of soybean plants (Abaca variety) growing separately in pots in controlled phytotron conditions (25/18 °C, photoperiod 16/8 h day/night, with photosynthetic photon flux density (PPFD) at a plant level of 500–700 µmol m −2 s −1 and 75% relative humidity). The plants were divided into four groups based on the biostimulant used: control (water only), untreated biostimulant, GA biostimulant and MW biostimulant. Each group consisted of 18 plants. The soybean seeds were pregerminated for three days on a moist filter paper. Subsequently, the seeds were sown into a sterile sowing substrate and were grown for a total of 24 days. First application of biostimulants (or water) was applied on 14th day in the form of spraying. After three days (day 17), first nine plants were harvested to collect samples for the analyses. The second spraying of the biostimulants was performed on day 21. Again after three days (day 24), the remaining nine plants were harvested to obtain samples for the analyses. Roots and aerial parts of the plants were separated in both sampling points. The experimental design is shown in Fig.  1 .

Every treatment was analyzed in three biological replicates, with each sample consisting of pooled material from three randomly chosen independent plants. Collected samples of leaves and roots were immediately frozen in liquid nitrogen and stored at -80°C until further analysis.

RNA extraction and cDNA synthesis

RNA extraction, reverse transcription, and RT-qPCR reactions were performed using standard protocols which were described in our previous studies [ 31 ]. In short, collected samples were immediately homogenized in liquid nitrogen using a sterile mortar and pestle. The isolation of total RNA was performed using TRIzol reagent (Invitrogen) according to the manufacturer’s recommendations. Integrity and quality of RNA samples were evaluated electrophoretically on 1.5% agarose gel and spectrophotometrically with NanoDrop2000 (Thermo Scientific™). The Maxima First Strand cDNA Synthesis Kit for RT-qPCR, with dsDNase (Thermo Scientific™) was used to remove the genomic DNA contamination and conduct the reaction of reverse transcription. The cDNA synthesis was carried out in a final volume of 20 µl using 3 µg of RNA. Obtained cDNA was used as a template in the following RT-qPCR reactions. The good quality of cDNA samples was confirmed via RT-qPCR reactions by analysis of amplification plots, mean Cq values, melt curves, and standard curves. Lack of genomic contamination in the samples was confirmed by NRT controls (no reverse transcriptase control).

RT-qPCR reactions and data analysis

Based on the literature review, five commonly used reference genes CYP (cyclophilin type peptidyl-prolyl cis–trans isomerase), EF1A (elongation factor 1-alpha), EF1B (elongation factor 1-beta), F-box (F-box protein), TUA (tubulin alpha) and five recently identified candidates showing stable expression in soybean Bic-C2 (RNA-binding protein Bicaudal-C), GPX (glutathione peroxidase), IGPS (indole-3-glycerol-phosphate synthase), TIA (apoptosis-promoting RNA-binding protein TIA-1/TIAR), ZnF (zinc finger) were chosen for the evaluation (Table  1 ) [ 28 , 32 , 33 , 34 ]. Some of the traditionally used reference genes exhibited rather poor expression stability in several reports (e.g., GAPDH [ 32 , 35 ], UBQ10 [ 36 , 37 ]), therefore along testing the most promising conventional controls, candidates emerging from RNA-seq data [ 33 , 34 ] were also included in the experimental setup.

Soybean CDS sequences and gene annotation data were retrieved from Phytozome (Phytozome genome ID: 275, annotation version: Glycine max Wm82.a2.v1) [ 30 , 38 ]. Primers for RT-qPCR were designed with the PrimerBLAST tool (Supplementary Table S1) [ 39 ]. The study employed only primer pairs which showed specific amplification (confirmed with dissociation curve analysis—Supplementary Figure S2) and displayed amplification efficiency of 90–110% and correlation coefficient (R 2 ) over 0.990 (determined via standard curve analysis). The RT-qPCR reactions were performed on the QuantStudio™ 3 apparatus (Applied Biosystems) using PowerUp™ SYBR™ Green Master Mix (Applied Biosystems™). The reactions were conducted in three technical replicates on 20 ng of template cDNA and 400 nM of each primer in 20 µl total volume, using the cycling profile recommended by the supplier.

Gene expression stability was determined using geNorm [ 44 ], NormFinder [ 45 ], BestKeeper (46), and ΔCt method [ 47 ]. Obtained results were subsequently compiled into the overall ranking generated as described in Velada et al. [ 48 ]. In short, each gene was assigned a weight according to its stability as assessed by abovementioned algorithms (weight of 1 assigned to the best-performing gene, weight of 10 assigned to the worst-peforming gene). Next, the geometric means of these weights were calculated and the comprehensive ranking was obtained. Three datasets were used in the analysis—the roots samples dataset, the leaves samples dataset, and the full dataset comprising all roots and leaves samples analyzed together. For the validation of selected reference genes, the expression level of target gene SOD encoding superoxide dismutase [Cu/Zn] (NCBI Reference Sequence: NM_001249007.3) was analyzed under tested experimental conditions. The RT-qPCR reactions were conducted as described above using the following primers F: TCCTCTCACTGGACCAAACAA and R: TCATGACCACCTTTCCCAAGATCA. The transcript level of SOD was normalized against the best-performing and the worst-performing candidate reference genes according to the obtained results. The relative expression level of the target gene was calculated using the 2 −∆∆Ct method with control samples being used as calibrator.

Determination of candidate RGs expression stability

The average expression stability value (M) of reference genes was calculated by the geNorm algorithm. Candidate genes showing stable expression in the tested material have low M values, while those showing variable expression are characterized by high M values [ 44 ]. As shown in Fig.  2 a, Bic-C2 and CYP were the best-performing pair of genes across all tested samples in this experiment, while EF1B was the worst-performing gene in the full dataset. In the leaves of the soybean plants treated with various biostimulants, the highest expression stability was exhibited by F-box and ZnF , while in the roots CYP and EF1A were considered to be the most stable (Supplementary Table S2).

Expression stability of RGs in soybean plants after biostimulant application (full dataset) evaluated by: a geNorm algorithm (based on expression stability values M); b NormFinder algorithm (based on stability values SV); c BestKeeper algorithm (based on correlation coefficients r )

Analysis performed by NormFinder includes intra- and intergroup variations in the calculation of the stability values (SV), with low SV indicating low expression variability [ 45 ]. According to the obtained results, Bic-C2 was identified as the best-scoring gene in full dataset (Fig.  2 b) and ranked as second-best when roots and leaf samples were analyzed separately. In both of these datasets, the lowest variation of expression among all tested candidates was demonstrated by F-box (Supplementary Table S2).

The expression stability of tested genes was subsequently evaluated by the BestKeeper algorithm, which determines the correlation coefficient (r) of each candidate with the BestKeeper index (the geometric mean of all candidate genes). High values of correlation coefficient indicate high expression stability of the gene [ 46 ]. On the other hand, the ΔCt method ranks the genes based on the average standard deviation (mean SD). Both BestKeeper and ΔCt method produced identical results regarding the two most stably expressed genes in a given dataset. In full dataset, Bic-C2 and CYP were identified as most stable after the biostimulants treatment. Likewise, both calculation methods indicated EF1B as the worst reference gene among all candidates. In the leaves dataset and roots dataset, F-box together with Bic-C2 was designated as the two most stable reference genes. Nevertheless, the gene order in stability rankings generated by both approaches varied in further positions (Fig.  2 c, Supplementary Table S2).

To determine how many reference genes should be used for reliable normalization of RT-qPCR data, the additional analysis of pairwise variation (V n / n+1 ), was performed in the geNorm algorithm. The pairwise variation value below 0.15 indicates no need for the inclusion of an additional reference [ 44 ]. Results obtained in the present study show that a pair of best-performing reference genes is sufficient for accurate normalization of the expression data, regardless of the samples being analyzed in full or separate datasets (Supplementary Figure S3.).

As a final point, the obtained results were compiled into a comprehensive ranking (Table  2 ). In general, F-box and Bic-C2 were the most stable reference genes in the soybean leaves subjected to the biostimulants treatment. Out of all tested genes, the expression of TUA was the most affected by the experimental conditions used in this study. In roots, Bic-C2 was shown to display more stable expression than F-box , while expression of IGPS fluctuated the most among a tested set of candidate genes. Nevertheless, for the experiments involving both leaves and roots samples of soybean, Bic-C2 together with CYP is recommended as the best pair of controls for the normalization of RT-qPCR data.

Validation of candidate reference genes

The expression of SOD gene was estimated using the best- and the worst-performing reference genes identified in this study. When Bic-C2 and CYP were used as internal controls, the SOD expression in leaves of plants sprayed with different variants of biostimulant remained stable (Fig.  3 a). However, when EF1B was used for data normalization, obtained results suggested downregulation of SOD transcription. Moreover, contradictory trends were shown in the roots (Fig.  3 b) of the plants treated with biostimulant activated with cold plasma. Depending on the reference genes used in the calculation, SOD expression was either upregulated or downregulated by biostimulant application. This demonstrates the significance of proper reference gene selection and the influence it might have on the reliability of observed expression changes of genes of interest.

The expression profiles of SOD gene in soybean plants after biostimulant treatment. Relative expression level in leaves after first treatment ( a ) and in roots after second treatment ( b ). Normalization was performed using a pair of the most stable reference genes ( Bic-C2  +  CYP ) in comparison to the most unstable reference gene ( EF1B ). Control: water (used as calibrator), Bio: untreated biostimulant, Bio MW: microwave plasma pre-treated biostimulant, Bio GA: gliding arc plasma pre-treated biostimulant. Data represents mean ± SD (n = 3), asterisk represents significant difference ( P  < 0.05, student’s t-test)

Biostimulatory effects of plant extracts have been extensively studied in recent years as an alternative approach for promoting crop growth in sustainable agricultural production [ 3 , 49 , 50 , 51 , 52 ]. Until now, no relevant studies have described the use of horsetail, dog rose, and soapwort in biostimulants to promote soybean plants growth and stress tolerance. Horsetail, as the main component of biostimulant in this study, is often used in organic farming for various claimed activities [ 53 ]. Its use in plant protection products was approved under Regulation of European Commission No. 1107/2009 as a basic substance since it has a preventive effect against fungal diseases due to its high silicon content. As a practical example, horsetail macerate was shown to be a promising Cu-free fungicide effective in protecting tomato plants against late blight [ 54 ]. Another study reported that horsetail extract increased the yield and improved the composition of basil essential oil, suggesting its positive effect on the quality of medicinal plants [ 55 ]. Polyphenol-rich rosehips, on the other hand, are neither commonly used nor approved for biostimulant production. However, polyphenol-based biostimulants can positively affect plant growth, especially at the root level [ 56 ], which is in coherence with using rosehip extracts as the biostimulant constituent in this study. Soapwort extract, as the material loaded with various saponins, is supposed to serve as an adjuvant in biostimulant. It occurs in this research-related biostimulant only to a minor extent, and its main purpose is to increase the efficiency of biostimulant. However, due to its properties, it may also partly act as a biocide. Application of common soapwort in biostimulants has not yet been reported in the scientific literature.

Better understanding of biostimulants’ mode of action requires complex studies conducted on a molecular level [ 57 , 58 ]. Investigating transcriptional changes occurring after the exogenous application of biostimulatory substances might provide insight into the complex processes leading to the beneficial effects of improved growth, yield and increased resistance to adverse environmental factors [ 57 , 58 ]. The RT-qPCR is a valuable and precise tool for evaluating changes in gene expression. However, in order to obtain accurate results proper data normalization is required. Thus, the step of reference genes selection is crucial in every experiment involving this technique [ 59 , 60 ].

Previous studies conducted on soybean regarding reference gene selection focused on evaluating candidate genes’ stability under various abiotic and biotic stresses, in different organs, cultivars, or developmental stages [ 28 , 32 , 37 , 40 , 41 , 42 ]. Although these results were obtained within the same species, they often are inconsistent or even contradictory, which might be attributed to a particular experimental setup. After testing soybean under different conditions, Wan et al. [ 28 ] reported that not a single gene displayed constant expression across all samples. Consequently, as ideal reference might not exist [ 61 ], it becomes crucial to precede each gene expression experiment with the identification of proper internal controls.

In this study, we evaluated the expression stability changes occurring in soybean plants subjected to foliar application of different variants of biostimulants. The experimental setup involved expression analysis of genes in both leaves and roots. We tested a set of ten potential reference genes—half of them represented commonly used internal controls ( CYP , EF1A , EF1B , F-box , TUA ), half comprised less-known but promising candidates ( Bic-C2, GPX, IGPS, TIA, ZnF ). Obtained data were analyzed via four different approaches (geNorm, NormFinder, BestKeeper, and ΔCt method), and the results were compiled into a comprehensive ranking of gene expression stability in leaves samples, roots samples and in the full dataset.

The results show that, regardless of the dataset, a pair of best-performing genes would be sufficient for gene expression normalization. Overall, Bic-C2 and CYP outperformed all other tested candidates in terms of expression stability in whole plants after biostimulants treatment. In fact, CYP was previously reported as being the most stable in different soybean organs [ 29 ], which corroborates our results. At the same time, when the leaves and roots samples were analyzed separately, other gene than CYP was classified as better candidate for data normalization. Along Bic-C2 , high expression stability in sample subgroups was exhibited by F-box . In the study by Sharma et al. [ 43 ], F-box also showed stable expression in both root and shoot samples of soybean exposed to macronutrient stress (irrespective of the datasets being analyzed together or separately). Likewise, F-box was reported to display stable expression in soybean under other abiotic stresses, such as high salinity (shoots), low temperature (shoots) and dehydration (roots and shoots) [ 40 ].

To our best knowledge, this is the first comprehensive study of reference genes stability in plants subjected to biostimulants treatment. Even though gene expression changes in plants caused by biostimulants have been reported before, typically one [ 62 , 63 , 64 ] or at best three traditional reference genes [ 65 , 66 , 67 ] were used for data normalization without previous confirmation of their stability in the given experimental setup. Only few studies report testing a small set of three [ 68 ] or four [ 69 ] internal control candidates before analyzing genes of interest. Using unverified internal controls poses a risk of miscalculating the real expression changes of genes of interest and drawing false conclusions [ 70 ]. Therefore, the step of identification of accurate and reliable reference genes should not be omitted in gene expression studies. For instance, here F-box was shown to display the highest expression stability in the soybean leaves treated with biostimulants. Similarly, it was reported as the most stable gene in soybean subjected to viral stress [ 32 ]. Nevertheless, in the leaves of soybean exposed to Cd stress, it was ranked as the most unstable candidate [ 42 ]. Likewise, ACT was reported as highly stable in adzuki bean ( Vigna angularis ) under waterlogging stress and rust infection [ 71 ], yet it performed poorly when the plants of this species were growing under iron deficiency [ 72 ].

Many times some of the traditionally used reference genes, such as GAPDH , have been proven to show rather poor expression stability under given experimental conditions [ 32 , 35 , 73 ]. Therefore, there’s a need to find new candidates for reliable reference. Using RNA-seq datasets might significantly aid in this process. Transcriptome-based identification of novel reference genes has already been conducted in some plant species, e.g., Gossypium hirsutum [ 74 ], Allium tuberosum [ 75 ] , Lactuca sativa [ 76 ] or Ardisia kteniophylla [ 77 ]. Yim et al. [ 33 ] also employed such strategy in order to find better internal controls for soybean studies. One of their newly identified candidates, Bic-C2 , outperformed all of the genes tested in this experiment. Another one, GPX , also showed good overall performance as it ranked third in the full dataset. Likewise, Machado et al. [ 34 ] analyzed 1298 RNA-seq soybean samples and found 452 genes displaying uniform and constant expression, which might potentially serve as reference gene candidates. Six of them were also tested in this study. While CYP , EF1A and GPX remained stable after biostimulants exposure across both leaves and roots, ZnF , TIA and IGPS exhibited rather average expression stability. Therefore, the verification of candidates emerging from RNA-seq is still needed.

Since being identified as constitutively expressed in soybean [ 33 ], Bic-C2 has been used several times for RT-qPCR data normalization [ 78 ]. Yet, until now, its stability has not been confirmed by other authors. Based on the obtained results, we recommend Bic-C2 to be used in pair with CYP as reliable internal control in biostimulant-soybean research and to be considered as a worthy candidate for studies conducted in different species.

In summary, in this experiment, we tested the expression stability of ten candidate genes in soybean plants treated with three novel variants of plant biostimulants. The selected candidate genes included five commonly used reference genes: CYP , EF1A , EF1B , F-box , TUA ; and five recently identified candidates showing stable expression in soybean: Bic-C2 , GPX , IGPS , TIA , ZnF . Comprehensive analysis conducted with four algorithms points to Bic-C2 and CYP as the best-performing pair of reference genes in tested experimental material. The lowest expression stability was shown by one of the traditionally used reference genes, EF1B . Our results confirm that a pair of best-scoring genes will be sufficient for reliable RT-qPCR data normalization. Overall, we recommend Bic-C2 to be used together with CYP as a good internal control in the research of biostimulant applications on soybean plants. Moreover, these two candidate genes could be considered for biostimulation studies conducted in other plant species. The results of this study will aid in elucidating the biostimulant’s mode of action on the transcriptional level.

Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the first author (Magdalena Sozoniuk) on reasonable request.

Abbreviations

RNA-binding protein Bicaudal-C

Complementary deoxyribonucleic acid

Coding DNA sequence

Cyclophilin type peptidyl-prolyl cis–trans isomerase

Deoxyribonucleic acid

Elongation factor 1-alpha

Elongation factor 1-beta

F-box protein

Gliding arc atmospheric plasma discharge

Glutathione peroxidase

Indole-3-glycerol-phosphate synthase

Microwave plasma discharge

Photosynthetic photon flux density

• Reference genes

Ribonucleic acid

Reverse transcription quantitative polymerase chain reaction

Superoxide dismutase

Stability value

Apoptosis-promoting RNA-binding protein TIA-1/TIAR

Tubulin alpha

Zinc finger

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Acknowledgements

This study was supported by Polish National Agency for Academic Exchange in frame of the project “Crucial, long-term collaborations for the development of an innovative, ecological approach in biostimulants production” (BPI/PST/2021/1/00034/U/00001).

This research was solely funded by the Polish National Agency for Academic Exchange, project No. BPI/PST/2021/1/00034/U/00001 “Crucial, long-term collaborations for the development of an innovative, ecological approach in biostimulants production”.

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Magdalena Sozoniuk

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Michał Świeca

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Andrea Bohatá, Markéta Jarošová, Adéla Stupková, Jana Lencová & Jan Bárta

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Agnieszka Szparaga

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MS: conceptualization, methodology, formal analysis, investigation, writing—original draft; MŚ: writing—review and editing; AB: funding acquisition, project administration, supervision; PB: funding acquisition, project administration; JB (Jan Bedrníček): investigation, writing—review and editing, visualization; FL: writing—review and editing, investigation; MJ: investigation; KP: investigation; AS (Adéla Stupková): investigation; JL: investigation; PO: investigation; JB (Jan Bárta): project administration; AS (Agnieszka Szparaga): investigation; methodology; MCPP: investigation; SK: funding acquisition, project administration, supervision. All authors read and approved the final manuscript.

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Sozoniuk, M., Świeca, M., Bohatá, A. et al. Selection of reference genes for expression profiling in biostimulation research of soybean. Chem. Biol. Technol. Agric. 11 , 130 (2024). https://doi.org/10.1186/s40538-024-00660-3

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DOI : https://doi.org/10.1186/s40538-024-00660-3

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